阅读说明：本技术 自动扶梯监测系统、方法、声音数据收集装置及用于其的夹具 (Escalator monitoring system, escalator monitoring method, sound data collection device and clamp used for escalator monitoring device ) 是由 叶涛 邓文科 张超 于 2017-12-29 设计创作，主要内容包括：一种自动扶梯监测系统,包括数据收集装置(1),设置在自动扶梯的需要监测的部件附近,以收集与自动扶梯的操作相关的部件的数据；数据发送装置(4),用于发送收集的数据；以及云端处理器(5),用于接收数据,并且将数据与存储在云端处理器(5)的数据库中的正常运转条件下获得的部件的阈值数据相比较,并且基于比较结果做出响应。自动扶梯监测系统可以在任何环境下,收集数据并对数据进行分析,并且不依赖人力的主观判断,在无需停止自动扶梯的操作的情况下提前预测可能的故障,节约维护时间,降低相关成本,以及提高电梯的安全性和乘坐舒适性。(An escalator monitoring system comprising a data collection device (1) disposed adjacent a component of the escalator to be monitored to collect data about the component relating to the operation of the escalator; data transmission means (4) for transmitting the collected data; and a cloud processor (5) for receiving the data and comparing the data with threshold data of the component obtained under normal operating conditions stored in a database of the cloud processor (5) and responding based on the comparison result. The escalator monitoring system can collect and analyze data in any environment, does not depend on subjective judgment of manpower, predicts possible faults in advance under the condition of not stopping the operation of the escalator, saves maintenance time, reduces related cost, and improves the safety and riding comfort of the escalator.)

1. An escalator monitoring system comprises a data collecting device, a data processing device and a monitoring device, wherein the data collecting device is arranged near a part needing to be monitored of an escalator and is used for collecting data of the part; the data transmitting device is used for transmitting data related to the safe operation of the escalator; and the cloud processor is used for receiving data related to safe operation of the escalator, comparing the data with a threshold value which is stored in the cloud processor and is obtained from the part under the normal operation condition, and responding based on the comparison result.

2. The escalator monitoring system of claim 1, wherein the data collection device is a sound data collection device, a temperature collection device, a power collection device, or a vibration collection device.

3. The escalator monitoring system according to claim 1 or 2, further comprising a local data processing device, wherein the data relating to safe operation of the escalator is processed by the local data processing device.

4. Escalator monitoring system according to claim 3, wherein the local data processing device calculates characteristic values of the data as data relating to the safe operation of the escalator.

5. The escalator lubrication status monitoring system according to claim 4, wherein if the comparison result is greater than the threshold value, the local data processing device issues an alarm signal regarding safe operation of the escalator or sends the comparison result to a cloud processor.

6. The escalator monitoring system according to claim 1 or 2, wherein the data relating to safe operation of the escalator is data collected by a data collection device, which data is received, processed by a cloud processor, and then compared to a threshold value stored in the cloud processor and obtained from components in normal operating conditions.

7. The escalator monitoring system of claim 6, wherein the cloud processor calculates the characteristic values of the data as data relating to safe escalator operation and then compares them to thresholds stored in the cloud processor and obtained from components in normal operating conditions.

8. The escalator monitoring system of claim 7, wherein if the comparison is greater than the threshold, the cloud processor issues an alarm signal regarding safe escalator operation and triggers a customer service system or communicates directly with maintenance personnel for lubrication maintenance.

9. The escalator monitoring system of claim 1 or 2, wherein the cloud processor comprises an escalator database storing escalator operating history data, escalator parameters, and escalator maintenance history.

10. The escalator monitoring system of claim 1 or 2, wherein the cloud processor compares the processed data to thresholds by statistical, analytical, artificial intelligence, and machine learning methods.

11. The escalator monitoring system of claim 1 or 2, wherein the cloud processor stores component data relating to safe escalator operation.

12. The escalator monitoring system according to claim 1 or 2, wherein if the comparison result is greater than the threshold value, the cloud processor issues an alarm signal regarding escalator safety status and triggers the customer service system.

13. Escalator monitoring system according to claim 1 or 2, wherein the local data processing device is a microcomputer or a DSP chip.

14. The escalator monitoring system of claim 13, wherein the microcomputer transmits data to the cloud processor over a wireless network.

15. Escalator monitoring system according to claim 2, wherein the data collection devices are sound data collection devices and the local data processing device is obtained in [ F ] by means of a band-pass filter_{Is low in}，F_{Height of}]The sound data of the parts in the range, which are relevant to the safe operation of the escalator, are calculated, andthe root mean square value is sent to the cloud processor and compared to a threshold sound data value for the metal part that is stored in a database of the cloud processor and obtained when the metal part is operating under normal operating conditions.

16. The escalator monitoring system according to claim 2, wherein the data collection device is a sound data collection device disposed near the contact location of the component, i.e., the contact location of the drive device with the drive chain, the contact location of the drive chain with the drive sprocket, or the contact location of the step guide block with the skirt plate, for calculating a characteristic value of the data and sending the characteristic value to the cloud processor for comparison with a sound data threshold value of the metal component stored in a database of the cloud processor and obtained when the component is operating under normal operating conditions.

17. The escalator monitoring system according to claim 2, wherein the data collecting device is a sound data collecting device, and the cloud processor receives data sent by the data collecting device and obtains the data as [ F ] through a band-pass filter_{Is low in}，F_{Height of}]The method comprises the steps of determining data relating to safe escalator operation for contact components within range, calculating a root mean square value of the data, and comparing the root mean square value to a threshold sound data for metal components stored in a database of a cloud processor and acquired when the components are operating under normal operating conditions.

18. The escalator monitoring system of claim 2, wherein the data collection device is a voice data collection device, the cloud processor receives voice data sent by the data collection device, calculates a characteristic value of the data, and compares the characteristic value to a voice data threshold for the metal component stored in a database of the cloud processor and obtained when the component is operating under normal operating conditions.

19. The escalator monitoring system according to any one of claims 15-18, wherein if the sound data is from a group of escalators at the same location and has a similar sound data pattern, the local data processing device may select only one of the sound data for processing.

20. The escalator monitoring system according to claim 2, wherein the data collection device is a temperature sensor assembly for sensing ambient temperature and handrail back temperature of the escalator in a shutdown condition, a standby-speed operating condition, and a full-speed operating condition; the data transmitting device receives the ambient temperature and the handrail back surface temperature from the temperature sensor assembly and transmits the ambient temperature and the handrail back surface temperature to a local data processing device or a cloud processor at a predetermined frequency, the local data processing device or the cloud processor compares the ambient temperature and the handrail back surface temperature and obtains a temperature difference between the ambient temperature and the handrail back surface temperature, and wherein a predetermined temperature difference threshold is stored in the local data processing device or the cloud processor, and the local data processing device or the cloud processor will issue an alarm signal if the temperature difference between the ambient temperature and the handrail back surface temperature exceeds the predetermined temperature difference threshold.

21. The escalator monitoring system according to claim 2, wherein the data collecting device is a temperature sensor assembly for detecting the handrail back temperature and the ambient temperature of the escalator within a predetermined time period, the data sending device sends the detected ambient temperature and the handrail back temperature to the local data processing device or the cloud processor, and the local data processing device or the cloud processor compares the ambient temperature and the handrail back temperature to obtain a first average value and a first variance value of temperature differences between the ambient temperature and the handrail back temperature within the first predetermined time period.

22. The escalator monitoring system of claim 21, wherein the temperature sensor assembly further detects the ambient temperature and the handrail back temperature for another predetermined period of time, the data transmitter transmits the detected ambient temperature and the handrail back temperature to the local data processing device or the cloud processor, and the local data processing device or the cloud processor compares the ambient temperature and the handrail back temperature to obtain a second average value and a second variance value of the temperature difference between the handrail back temperature and the ambient temperature for the another predetermined period of time.

23. The escalator monitoring system of claim 22, wherein the local data processing device or cloud processor is configured to issue a first warning signal when the second average differs from the first average by more than k times the first variance, where k is an integer greater than 1 set in the local data processing device or cloud processor.

24. The escalator monitoring system of claim 23, wherein the local data processing device or cloud processor further determines a relationship of a first variance value to a predetermined first threshold value and a relationship of a second variance value to a predetermined second threshold value, and if the first variance value is less than the first threshold value and the second variance value is less than the second threshold value, the local data processing device or cloud processor will issue a second alarm signal.

25. The escalator monitoring system according to claim 2, wherein the data collecting device is a passenger flow sensor and a power sensor, the passenger flow sensor is configured to detect a passenger flow entering the escalator within a unit time, the power sensor is configured to detect power consumption within a unit time, the data sending unit sends the detected passenger flow within the unit time and the detected power consumption within the unit time to the local data processing device or the cloud processor, and the local data processing device or the cloud processor determines an operating state of the escalator according to a relationship between the passenger flow within the unit time and the power consumption within the unit time to determine whether an alarm signal needs to be sent.

26. The escalator monitoring system of claim 25, wherein the local data processing device or cloud processor calculates a ratio of power consumption per unit time to passenger flow per unit time, and if the ratio has an abnormal sudden change, the local data processing device or cloud processor sends an alarm signal; if the ratio is not abnormally abrupt and has an increasing trend within a preset time and is larger than a preset threshold value, the local data processing device or the cloud processor sends out an alarm signal.

27. The escalator monitoring system of claim 26, wherein the power sensor detects an average power over a predetermined period of time while the escalator is operating at a standby speed, and the local data processing device or cloud processor will issue an alarm signal if the average power does not fall within a predetermined threshold range stored in the local data processing device or cloud processor.

28. The escalator monitoring system of claim 25, wherein the local data processing device or cloud processor is further configured to issue an alarm signal if the traffic volume detected by the traffic volume sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the traffic volume is zero for a period of time exceeding a predetermined threshold.

29. The escalator monitoring system of claim 25, wherein the local data processing device or cloud processor is further configured to issue an alarm signal if the passenger flow rate detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is a standby power output for a period of time exceeding a predetermined threshold.

30. An escalator monitoring system comprises a sound data collecting device, a data collecting device and a data processing device, wherein the sound data collecting device is arranged near a part needing to be monitored of an escalator and is used for collecting data of the part; and the local data processing device is used for locally processing the sound data, sending the sound data to the cloud processor or responding to the result after locally processing the sound data.

31. An escalator monitoring system comprises a cloud processor for receiving sound data sent by a remote data collection device arranged near a component needing monitoring of the escalator, processing the sound data, and responding to the result after processing the data.

32. A sound data collection device according to any of claims 15 to 19, wherein the sound data collection device comprises a circuit board, one or more digital microphones, and a circuit box enclosing the circuit board and the digital microphones, the circuit box comprising a top wall, a bottom wall and side walls, at least one of the top and bottom walls being provided with a sound pick-up aperture.

33. A sound data collection device according to claim 32, wherein a water-resistant membrane is provided on the inside or outside of the wall in which the sound pick-up aperture is located to prevent water or moisture from entering the digital microphone.

34. A sound data collection device according to claim 33, wherein the digital microphone is provided on the inside of the wall in which the sound pick-up aperture is located, below the waterproof membrane, or on the circuit board.

35. A clamp for a temperature collection device of an escalator monitoring system as claimed in any one of claims 20-24, wherein the mounting position of the clamp is adjustable according to the drive mode of the handrail.

36. The clip for a temperature collection device of claim 35, wherein the clip is mounted on a C-shaped piece of an escalator skirt if a handrail is in a friction wheel drive mode.

37. The clamp for a temperature collection device of claim 36, wherein said clamp includes a bracket and an adjustable plate, the adjustable plate being mounted on the bracket, the bracket being mounted to a C-piece of the skirt, said temperature sensor assembly being mounted at an end of said adjustable plate distal from the C-piece.

38. The jig for a temperature collecting device according to claim 37, wherein one of the adjustable plate and the bracket has a first elongated hole extending in a length direction, the other of the adjustable plate and the bracket has an opening, and a connecting means passes through the first elongated hole and the opening to connect the adjustable plate and the bracket, and adjusts a distance of the temperature sensor assembly from the C-shaped member in the length direction according to an alignment position of the opening with the first elongated hole in the length direction.

39. The clamp for a temperature collection device of claim 35, wherein the clamp is mounted on a stanchion of an escalator when the handrail is in an end wheel drive mode.

40. A clamp for a temperature collection device according to claim 39, wherein said clamp includes a bracket and an adjustable plate, the adjustable plate being mounted on the bracket, said bracket being mounted on said post, said temperature sensor assembly being mounted at an end of said adjustable plate remote from the post.

41. The jig for a temperature collecting device according to claim 40, wherein one of the adjustable plate and the bracket has a second elongated hole extending in a width direction, the other of the adjustable plate and the bracket has a third elongated hole extending in a length direction, a connecting means passes through the second elongated hole and the third elongated hole to connect the adjustable plate and the bracket, and a distance of the temperature sensor assembly from the stay in the length direction and a distance of the temperature sensor assembly from the armrest in the width direction are adjusted according to alignment positions of the second elongated hole and the third elongated hole.

42. Escalator comprising an escalator monitoring system according to one of the preceding claims 1-31, a sound data collecting device according to one of the preceding claims 32-34 and/or a clamp for a temperature collecting device according to one of the preceding claims 35-41.

43. A monitoring method of an escalator comprises the following steps:

detecting the back temperature of the handrail and the ambient temperature of the escalator in a stop state, a standby speed running state and a full speed running state by using a temperature sensor component;

receiving the ambient temperature and the handrail back surface temperature from the temperature sensor assembly by using the data sending unit, and sending the ambient temperature and the handrail back surface temperature to a local data processing device or a cloud processor at a predetermined frequency;

wherein the local data processing device or the cloud processor compares the ambient temperature with the handrail back surface temperature and obtains a temperature difference between the ambient temperature and the handrail back surface temperature, and wherein a predetermined temperature difference threshold is stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor issuing an alarm signal if the temperature difference between the ambient temperature and the handrail back surface temperature exceeds the predetermined temperature difference threshold.

44. The method of claim 43, wherein the method further comprises: the method comprises the steps of capturing the handrail back temperature and the ambient temperature detected by the temperature sensor assembly within a preset time period by using the local data processing device or the cloud processor, and comparing the ambient temperature with the handrail back temperature to obtain a first average value and a first variance value of the temperature difference between the ambient temperature and the handrail back temperature within the preset time period.

45. The method of claim 44, wherein the method further comprises: the method comprises the steps of capturing the handrail back surface temperature and the ambient temperature detected by the temperature sensor assembly in another preset time period by using the local data processing device or the cloud processor, and comparing the ambient temperature with the handrail back surface temperature to obtain a second average value and a second variance value of the temperature difference between the ambient temperature and the handrail back surface temperature in the other preset time period.

46. The method of claim 45, wherein the local data processing device or cloud processor is configured to issue the first alarm signal when the second average differs from the first average by a value greater than k times the first variance, wherein k is an integer greater than 1 set in the local data processing device or cloud processor.

47. The method of claim 46, wherein the local data processing device or cloud processor further determines a relationship between the first variance value and a predetermined first threshold, and the second variance value and a predetermined second threshold, and if the first variance value is less than the first threshold and the second variance value is less than the second threshold, the local data processing device or cloud processor issues a second alarm.

48. A monitoring method of an escalator comprises the following steps:

detecting the passenger flow entering the escalator in unit time by using a passenger flow sensor;

detecting power consumption per unit time by using a power sensor;

transmitting the detected passenger flow volume in unit time and the detected power consumption in unit time to a local data processing device or a cloud processor by using a data transmitting unit;

the server judges the running state of the escalator according to the relation between the passenger flow in unit time and the power consumption in unit time so as to determine whether an alarm signal needs to be sent out.

49. The method of claim 48, wherein the local data processing device or the cloud processor calculates a ratio of power consumption per unit time to a passenger flow per unit time, and if the ratio has an abnormal sudden change, the local data processing device or the cloud processor sends out an alarm signal; if the ratio is not abnormally abrupt and has an increasing trend within a preset time and is larger than a preset threshold value, the local data processing device or the cloud processor sends out an alarm signal.

50. The method of claim 49, wherein the power sensor detects an average power over a predetermined period of time while the escalator is operating at a stand-by speed, and the local data processing device or cloud processor will issue an alarm signal if the average power does not fall within a predetermined threshold range stored in the local data processing device or cloud processor.

51. The method of claim 50, wherein the local data processing device or cloud processor is further configured to issue an alarm if the traffic volume detected by the traffic volume sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the traffic volume is zero for more than a predetermined threshold period of time.

52. The method of claim 50, wherein the local data processing device or cloud processor is further configured to issue an alarm signal if the traffic detected by the traffic sensor is not zero and the power consumption detected by the power sensor is a standby power output for a period of time exceeding a predetermined threshold.

Technical Field

The present invention relates to an escalator monitoring system, an escalator detection method, a sound data collecting device and a jig therefor, and more particularly, to an escalator monitoring system or an escalator detection method, a sound data collecting device and a jig therefor, which analyze an operating state of an escalator using data collected by the sound data collecting device.

Background

Escalators are widely used in a variety of settings, such as department stores, office buildings, public facilities, and the like, as well as in various indoor or outdoor environments. The safe and stable operation of the escalator needs to monitor the operation state of related components, and the operation state of the related components is closely related to the temperature, humidity and dust of the application occasion and environment of the escalator and is also closely related to the number of people and the use frequency. The condition of each escalator is therefore different and requires frequent field inspection and maintenance of the relevant components in order for them to function properly. This involves a lot of work and makes it difficult for the escalator manufacturer or user to keep track of its operating conditions in an organized manner.

To address this problem, there is a need for an improved escalator monitoring system that allows remote monitoring and generation of raw data for components associated with escalator ride. In addition, the system can process and analyze the original data, can rapidly judge the running condition of the escalator, can early warn possible faults of the escalator without depending on manpower, and can provide data generated during the running of the escalator for reference for manufacturers and users.

Disclosure of Invention

It is an object of the present invention to provide an escalator monitoring system, a data collection device and a clamp for the monitoring system and an escalator monitoring method which meet the above-mentioned needs.

According to a first aspect of the present invention, there is provided an escalator monitoring system comprising a data collection device disposed adjacent a component of an escalator to be monitored for collecting data from the component; the data transmitting device is used for transmitting data related to the safe operation of the escalator; and the cloud processor is used for receiving data related to safe operation of the escalator, comparing the data with a threshold value which is stored in the cloud processor and is obtained from the part under the normal operation condition, and responding based on the comparison result.

Preferably, the data collection device is a sound data collection device, a temperature collection device, a power collection device or a vibration collection device.

Preferably, the escalator monitoring system further comprises a local data processing device, and the data related to the safe operation of the escalator is the data processed by the local data processing device.

Preferably, the local data processing device calculates characteristic values of the data as data relating to safe operation of the escalator.

Preferably, if the comparison result is greater than the threshold value, the local data processing device sends out an alarm signal about safe operation of the escalator, or sends the comparison result to the cloud processor.

Preferably, the data relating to safe operation of the escalator is data collected by a data collection device, which is received and processed by a cloud processor, and then compared with a threshold value stored in the cloud processor and obtained from the component under normal operating conditions.

Preferably, the cloud processor calculates the characteristic values of the data as data relating to safe operation of the escalator, and then compares them with thresholds stored in the cloud processor and obtained from components under normal operating conditions.

Preferably, if the comparison result is greater than the threshold value, the cloud processor sends out an alarm signal about safe operation of the escalator, and triggers a customer service system or directly communicates with maintenance personnel to perform lubrication maintenance.

Preferably, the cloud processor comprises an escalator database, and the escalator operation history data, the escalator parameters and the escalator maintenance history are stored in the escalator database.

Preferably, the cloud processor compares the processed data to the threshold values by statistical, analytical, artificial intelligence, machine learning, and the like.

Preferably, the cloud processor stores data of the component relating to safe operation of the escalator.

Preferably, if the comparison result is greater than the threshold value, the cloud processor sends out an alarm signal about the safety state of the escalator, and triggers the customer service system.

Preferably, the local data processing means is a microcomputer or a DSP chip.

Preferably, the microcomputer transmits data to the cloud processor through a wireless network.

Preferably, the data collection device is a sound data collection device, and the local data processing device obtains sound data related to safe operation of the escalator for the part in the range of [ ftow, ftow ] through a band-pass filter, calculates a root mean square value of the data, and sends the root mean square value to the cloud processor to be compared with a sound data threshold value of the metal part stored in a database of the cloud processor and obtained when the metal part is operating under normal operating conditions.

Preferably, the data collection device is a sound data collection device disposed near the contact position of the component, i.e., the contact position of the drive device with the drive chain, the contact position of the drive chain with the drive sprocket, or the contact position of the step guide block with the skirt board, for calculating a characteristic value of the data and sending the characteristic value to the cloud processor for comparison with a sound data threshold value of the metal component stored in a database of the cloud processor and acquired when the component is operating under normal operating conditions.

Preferably, the data collection device is a sound data collection device, the cloud processor receives the data sent by the data collection device, obtains data related to safe operation of the escalator for the contact part in the range of [ ftow, ftow ] through the band-pass filter, calculates a root mean square value of the data, and compares the root mean square value with a sound data threshold value of the metal part stored in a database of the cloud processor and obtained when the part is operating under normal operating conditions.

Preferably, the data collection device is a voice data collection device, and the cloud processor receives voice data sent by the data collection device, calculates a characteristic value of the data, and compares the characteristic value with a voice data threshold value of the metal part stored in a database of the cloud processor and obtained when the part is operating under normal operating conditions.

Preferably, if the sound data is from a group of escalators at the same location and has a similar sound data pattern, the local data processing device may select only one of the sound data for processing.

Preferably, the data collection device is a temperature sensor assembly for detecting the ambient temperature and the handrail back temperature of the escalator in a stop state, a standby speed running state and a full speed running state; the data transmitting device receives the ambient temperature and the handrail back surface temperature from the temperature sensor assembly and transmits the ambient temperature and the handrail back surface temperature to a local data processing device or a cloud processor at a predetermined frequency, the local data processing device or the cloud processor compares the ambient temperature and the handrail back surface temperature and obtains a temperature difference between the ambient temperature and the handrail back surface temperature, and wherein a predetermined temperature difference threshold is stored in the local data processing device or the cloud processor, and the local data processing device or the cloud processor will issue an alarm signal if the temperature difference between the ambient temperature and the handrail back surface temperature exceeds the predetermined temperature difference threshold.

Preferably, the data collecting device is a temperature sensor assembly for detecting the handrail back surface temperature and the ambient temperature of the escalator within a predetermined time period, the data sending device sends the detected ambient temperature and the handrail back surface temperature to the local data processing device or the cloud processor, and the local data processing device or the cloud processor compares the ambient temperature and the handrail back surface temperature to obtain a first average value and a first variance value of temperature differences between the ambient temperature and the handrail back surface temperature within the first predetermined time period.

Preferably, the temperature sensor assembly further detects an ambient temperature and an armrest back surface temperature within another predetermined period of time, the data sending device sends the detected ambient temperature and the armrest back surface temperature to the local data processing device or the cloud processor, and the local data processing device or the cloud processor compares the ambient temperature with the armrest back surface temperature to obtain a second average value and a second variance value of a temperature difference between the armrest back surface temperature and the ambient temperature within the another predetermined period of time.

Preferably, the local data processing device or the cloud processor is configured to issue the first alarm signal when a difference between the second average value and the first average value is greater than k times of the first variance value, where k is an integer greater than 1 set in the local data processing device or the cloud processor.

Preferably, the local data processing device or the cloud processor further determines a relationship between the first variance value and a predetermined first threshold, and a relationship between the second variance value and a predetermined second threshold, and if the first variance value is smaller than the first threshold and the second variance value is smaller than the second threshold, the local data processing device or the cloud processor sends a second alarm signal.

Preferably, the data collection device is a passenger flow sensor and a power sensor, the passenger flow sensor is used for detecting the passenger flow entering the escalator in unit time, the power sensor is used for detecting the power consumption in unit time, the data sending unit sends the detected passenger flow in unit time and the detected power consumption in unit time to the local data processing device or the cloud processor, and the local data processing device or the cloud processor judges the running state of the escalator according to the relation between the passenger flow in unit time and the power consumption in unit time so as to determine whether an alarm signal needs to be sent.

Preferably, the local data processing device or the cloud processor calculates a ratio of power consumption in unit time to passenger flow in unit time, and if the ratio has an abnormal sudden change, the local data processing device or the cloud processor sends an alarm signal; if the ratio is not abnormally abrupt and has an increasing trend within a preset time and is larger than a preset threshold value, the local data processing device or the cloud processor sends out an alarm signal.

Preferably, when the escalator is running at a standby speed, the power sensor detects the average power within a preset time period, and if the average power does not fall into a preset threshold range stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor sends out an alarm signal.

Preferably, the local data processing device or the cloud processor is further configured to send an alarm signal if the passenger flow rate detected by the passenger flow rate sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the passenger flow rate is zero within a period exceeding a predetermined threshold.

Preferably, the local data processing device or the cloud processor is further configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is the standby power output within a period exceeding a predetermined threshold.

According to a second aspect, there is provided an escalator monitoring system comprising a sound data collecting device disposed adjacent a component of an escalator to be monitored for collecting data of the component; and the local data processing device is used for locally processing the sound data, sending the sound data to the cloud processor or responding to the result after locally processing the sound data.

According to a third aspect, there is provided an escalator monitoring system comprising a cloud processor for receiving sound data transmitted by a remote data collection device disposed in the vicinity of a component of the escalator to be monitored, processing the sound data, and responding to the result after processing the data.

According to a fourth aspect, there is provided a sound data collection device comprising a circuit board, one or more digital microphones, and a circuit box enclosing the circuit board and the digital microphones, the circuit box comprising a top wall, a bottom wall and side walls, at least one of the top wall and the bottom wall being provided with a sound pickup aperture.

Preferably, a waterproof film is provided on the inner or outer side of the wall where the sound pickup hole is located to prevent water or moisture from entering the digital microphone.

Preferably, the digital microphone is disposed inside a wall where the sound pickup hole is located, below the waterproof film, or on the circuit board.

According to a fifth aspect, there is provided a clamp for a temperature collection device of an escalator monitoring system, wherein a mounting position of the clamp is adjustable according to a drive mode of a handrail.

Preferably, the clip is mounted on a C-shaped member of the skirt panel of the escalator if the handrail is in a friction wheel drive mode.

Preferably, the clamp comprises a bracket and an adjustable plate, the adjustable plate is mounted on the bracket, the bracket is mounted on a C-shaped part of the apron, and the temperature sensor assembly is mounted at one end of the adjustable plate, which is far away from the C-shaped part.

Preferably, one of the adjustable plate and the bracket has a first elongated hole extending in a length direction, the other of the adjustable plate and the bracket has an opening, and a connecting means passes through the first elongated hole and the opening to connect the adjustable plate and the bracket, and adjusts a distance of the temperature sensor assembly from the C-shaped member in the length direction according to an alignment position of the opening with the first elongated hole in the length direction.

Preferably, the clamp is mounted on the post of the escalator when the handrail is in the end-wheel drive mode.

Preferably, the clamp includes a bracket and an adjustable plate, the adjustable plate being mounted on the bracket, the bracket being mounted on the post, the temperature sensor assembly being mounted at an end of the adjustable plate remote from the post.

Preferably, one of the adjustable plate and the bracket has a second elongated hole extending in a width direction, the other of the adjustable plate and the bracket has a third elongated hole extending in a length direction, a connecting means passes through the second elongated hole and the third elongated hole to connect the adjustable plate and the bracket, and a distance of the temperature sensor assembly from the pillar in the length direction and a distance of the temperature sensor assembly from the armrest in the width direction are adjusted according to alignment positions of the second elongated hole and the third elongated hole.

According to a sixth aspect, an escalator is provided, comprising an escalator monitoring system as described above, a sound data collecting device as described above and/or a clamp as described above.

According to a seventh aspect, there is provided a method of monitoring an escalator, comprising the steps of:

detecting the back temperature of the handrail and the ambient temperature of the escalator in a stop state, a standby speed running state and a full speed running state by using a temperature sensor component;

receiving the ambient temperature and the handrail back surface temperature from the temperature sensor assembly by using the data sending unit, and sending the ambient temperature and the handrail back surface temperature to a local data processing device or a cloud processor at a predetermined frequency;

wherein the local data processing device or the cloud processor compares the ambient temperature with the handrail back surface temperature and obtains a temperature difference between the ambient temperature and the handrail back surface temperature, and wherein a predetermined temperature difference threshold is stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor issuing an alarm signal if the temperature difference between the ambient temperature and the handrail back surface temperature exceeds the predetermined temperature difference threshold.

Preferably, the method further comprises: the method comprises the steps of capturing the handrail back temperature and the ambient temperature detected by the temperature sensor assembly within a preset time period by using the local data processing device or the cloud processor, and comparing the ambient temperature with the handrail back temperature to obtain a first average value and a first variance value of the temperature difference between the ambient temperature and the handrail back temperature within the preset time period.

Preferably, the method further comprises: the method comprises the steps of capturing the handrail back surface temperature and the ambient temperature detected by the temperature sensor assembly in another preset time period by using the local data processing device or the cloud processor, and comparing the ambient temperature with the handrail back surface temperature to obtain a second average value and a second variance value of the temperature difference between the ambient temperature and the handrail back surface temperature in the other preset time period.

Preferably, the local data processing device or the cloud processor is configured to send out the first alarm signal when a difference between the second average value and the first average value is greater than k times of the first variance value, where k is an integer greater than 1 set in the local data processing device or the cloud processor.

Preferably, the local data processing device or the cloud processor further determines a relationship between the first variance value and a predetermined first threshold, and a relationship between the second variance value and a predetermined second threshold, and if the first variance value is smaller than the first threshold and the second variance value is smaller than the second threshold, the local data processing device or the cloud processor sends a second alarm signal.

According to an eighth aspect, there is provided a method of monitoring an escalator, comprising the steps of:

detecting the passenger flow entering the escalator in unit time by using a passenger flow sensor;

detecting power consumption per unit time by using a power sensor;

transmitting the detected passenger flow volume in unit time and the detected power consumption in unit time to a local data processing device or a cloud processor by using a data transmitting unit;

the server judges the running state of the escalator according to the relation between the passenger flow in unit time and the power consumption in unit time so as to determine whether an alarm signal needs to be sent out.

Preferably, the local data processing device or the cloud processor calculates a ratio of power consumption in unit time to passenger flow in unit time, and if the ratio has an abnormal sudden change, the local data processing device or the cloud processor sends an alarm signal; if the ratio is not abnormally abrupt and has an increasing trend within a preset time and is larger than a preset threshold value, the local data processing device or the cloud processor sends out an alarm signal.

Preferably, when the escalator is running at a standby speed, the power sensor detects the average power within a preset time period, and if the average power does not fall into a preset threshold range stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor sends out an alarm signal.

Preferably, the local data processing device or the cloud processor is further configured to send an alarm signal if the passenger flow rate detected by the passenger flow rate sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the passenger flow rate is zero within a period exceeding a predetermined threshold.

Preferably, the local data processing device or the cloud processor is further configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is the standby power output within a period exceeding a predetermined threshold.

The escalator detection method or the escalator detection system can acquire and analyze data in any environment, including indoor or outdoor, quiet and noisy environments, and does not depend on subjective judgment of manpower. The elevator fault prediction method and the elevator fault prediction device can accurately predict faults in advance under the condition of no need of stopping the elevator, save maintenance time and money, and improve the safety and riding comfort of the elevator.

Drawings

Fig. 1 is a flow diagram of an escalator monitoring system according to a first embodiment of the present invention.

Fig. 2 is a Spectrogram of a Spectrogram function illustrating a process of filtering out low-frequency noise and high-frequency noise by a band-pass filter.

Fig. 3 shows comparison of the root mean square value of the sound data calculated in the first embodiment with a threshold value.

Fig. 4 is a flow chart of an escalator monitoring system according to a second embodiment of the present invention.

Fig. 5 is a sound data pattern of different classes of faults.

Fig. 6 is a schematic analysis flow chart of the classifier 8.

Fig. 7 is a flow chart of an escalator monitoring system according to a third embodiment of the present invention.

Fig. 8 is a flow chart of an escalator monitoring system according to a fourth embodiment of the present invention.

Fig. 9 is a flow chart of an escalator monitoring system according to a fifth embodiment of the present invention.

Fig. 10 is a flow chart of an escalator monitoring system according to a sixth embodiment of the present invention.

Fig. 11 is a first embodiment of a sound data collection device 1 for use in an escalator monitoring system according to various embodiments of the present invention.

Fig. 12 is a second embodiment of the sound data collection device 1 for an escalator monitoring system according to various embodiments of the present invention.

Fig. 13 is a third embodiment of the sound data collection device 1 for an escalator monitoring system according to various embodiments of the present invention.

Fig. 14 is a fourth embodiment of the sound data collection device 1 for an escalator monitoring system according to various embodiments of the present invention.

Fig. 15 is a block diagram of an escalator monitoring system according to a seventh embodiment of the invention;

FIG. 16 shows a friction wheel drive mode;

FIG. 17 shows an end wheel drive mode;

FIG. 18 shows the clip installed on the C-section of the skirt in a friction wheel drive mode;

fig. 19 shows an enlarged view of a in fig. 18;

FIG. 20 shows a schematic view of the clamp of FIG. 18;

fig. 21 shows the temperature sensor assembly to be secured to the escalator by the clamp of fig. 18;

FIG. 22 shows the clamp mounted on a stanchion in an end wheel drive mode;

fig. 23 shows an enlarged view of B in fig. 18;

FIG. 24 shows a schematic view of the clamp of FIG. 22;

fig. 25 shows the temperature sensor assembly secured to the escalator by the clamp of fig. 22.

Fig. 26 shows a flow chart of a first embodiment of the monitoring method according to the invention.

Fig. 27 is a block diagram of an escalator monitoring system according to an eighth embodiment of the present invention.

Fig. 28 shows the ratio of power output per unit time to passenger flow per unit time.

FIG. 29 is a schematic diagram showing the relationship between passenger flow and motor power for the real-time monitoring device of the present invention;

FIG. 30 is a schematic view showing another relationship between passenger flow and motor power for the real-time monitoring device of the present invention;

fig. 31 shows a flow chart of a second embodiment of the monitoring method according to the invention.

Detailed Description

The escalator monitoring system avoids the occurrence of escalator faults through the data collection device, and performs predictive maintenance on the escalator faults, thereby reducing the labor and cost for maintaining the escalator. Data collection devices at or near escalator safety and normal operation related components collect data while the escalator is operating, analyze, judge, predict and determine the operating condition of the escalator, and respond to the determined operating condition.

The escalator monitoring system of the present invention can monitor the vibration state of the escalator drive or drive, the lubrication state between the key parts of the escalator that are in contact with each other, the temperature of the key parts of the escalator, the motor power of the escalator that is associated with varying passenger flow, and the like. The drive means or drive elements which need to be monitored for vibration conditions include the gear box, steps, etc. of the escalator. The vibration of the gear box and the step is different in the normal operation state and the abnormal state, and the vibration data reflecting the vibration difference can be used for analyzing, predicting and determining the operation state of the escalator, so that predictive maintenance is realized. The components between which the lubrication condition needs to be monitored include the drive means and drive chain, the drive chain and drive sprocket, and the step guide blocks and skirt panels. These parts are metallic and move relative to each other. With sufficient lubrication therebetween, these metal parts move smoothly and emit uniform and small sound. In the state of lack of lubricating oil or the state of rusting and dust spreading, the movement becomes less smooth and the harsh noise is generated. Thus, the acoustic data collected therefrom can be used to monitor the adequacy of lubrication, thereby providing predictive maintenance by analyzing, predicting and determining the operating conditions of the escalator. Thermometers may be placed at critical components of the escalator to monitor temperature changes thereof to aid in the analysis, prediction, and determination of the operating conditions of the escalator to provide predictive maintenance. Key components include, but are not limited to, armrests. When their temperature varies within a normal range, the handrail will operate normally; otherwise, they may fail. The passenger flow affects the normal operation of the escalator. High passenger flow rates beyond the capacity of escalators can cause safety problems. Also, the passenger flow volume can be evaluated based on the motor power of the escalator. Thus, the motor power can be used to analyze, predict and determine the operating conditions of the escalator, thereby providing predictive maintenance.

The present invention is described in further detail in the following examples.

Fig. 1 is a flow chart of an escalator monitoring system according to a first embodiment. In a first embodiment of the present invention, an escalator monitoring system monitors the lubrication state of an escalator, collects sound data emitted from metal parts using a data collection device, and analyzes the sound data to determine whether the lubrication state is normal. The data collection device is typically a digital microphone module that is placed near the contact location of the drive unit and drive chain of the escalator, near the contact location of the drive chain and drive sprocket of the escalator, or near the contact location of the step guide block and skirt below the step belt front cover, and may also be placed as desired on the truss of the escalator near the contact location of the motor and drive chain, or near any component that contacts and therefore needs to be monitored. The microphone module may use a general microphone or an ultrasonic microphone which is generally used in a cellular phone. It can record sound waves and support playback functions. The digital microphone module continuously collects sound data and stores sound files that can be played back when needed.

In fig. 1, the sound digital collecting device 1 collects sound data at a contact position between a driving device and a driving chain, a contact position between a driving sprocket of a gear box of an escalator and a driving chain, or a contact position between a step guide block and a skirt panel under a step band cowl. The sound file containing the sound data is stored at the local data processing apparatus 2 of the local apparatus 6 and processed by the band-pass filter 3. The file is then transmitted to the cloud processor 5 through the sound data transmission device 4.

At the band-pass filter 3, the sound data is denoised so as to obtain a frequency in a specific frequency range F_{Is low in},F_{Height of}]Internal sound data, F_{Is low in}Usually 1000-5000 Hz, F_{Height of}Typically greater than 10000Hz, since sound data in this range is most likely related to noise generated by metal-to-metal friction. This process can be performed by plotting the Spectrogram by the Spectrogram function.

The Spectrogram is plotted in fig. 2 schematically as the Spectrogram function of this procedure for filtering out low-frequency and high-frequency noise by means of band-pass filters. Fig. 2(a) shows an acoustic signal generated by contact between a normal chain and a sprocket without rusting, which is mainly focused on a low frequency range. Fig. 2(b) shows the acoustic signal produced by the contact between the rusted chain and the sprocket, which is a mixture of low frequency noise and high frequency peaks. Fig. 2(c) is a graph that retains only the isolated acoustic patterns associated with the rusty condition by applying a band pass filter in the frequency range [5000,15000 ]. Fig. 2(c) shows the sound pattern isolated after applying a band-pass filter in the frequency range [5000,15000] and retaining only those related to the degree of rust present on the chain.

The next step is to calculate Key Performance Indicators (KPIs) of the filtered sound data. The KPI associated with noise emanating from contact between metal pieces may be the Root Mean Square (RMS) value of the sound data. In this embodiment, the RMS value is used to calculate the peak value of the filtered sound data.

The RMS value is transmitted to the cloud processor 5 via the sound data transmission device 4. In the cloud processor 5, the RMS value is compared to a set threshold. If the RMS value is above the threshold, the cloud processor responds by sending an alarm signal to the customer service center or communicating directly with maintenance personnel for lubrication maintenance. This comparison is exemplarily shown in fig. 3.

The predetermined threshold value may be obtained by testing under various lubrication conditions or by other kinds of experiments.

Fig. 4 is a flow chart of an escalator monitoring system according to a second embodiment.

In the second embodiment, the digital collecting device 1 collects sound data at a contact position between the driving device and the driving chain, at a contact position between a driving sprocket of a gear box of the escalator and the driving chain, or at a contact position between a step guide block and a skirt panel below a step band front cowl. The sound file containing the sound data is stored at the local data processing apparatus 2 of the local apparatus 6. The local data processing apparatus 2 calculates a feature value of the sound data and then transmits it to the cloud processor 5 through the sound data transmitting device 4.

Fig. 5 shows a comparison between sound data of different fault types. Fig. 5(a) shows a sound data pattern generated when step misalignment occurs, fig. 5(b) shows a sound data pattern generated by a rusted drive chain, and fig. 5(c) shows a sound data pattern when the escalator is in a normal operation state. Obviously, different types of failure sound data have different patterns and therefore different feature values.

In this embodiment, the local data processing apparatus 2 calculates a feature value of sound data. The eigenvalues may be zero-crossing rates, energies, energy entropies, spectral centroids, spectral entropies, spectral fluxes, spectral roll-offs, spectral cepstral coefficients (MFCCs), chrominance vectors, or chrominance offsets.

The calculated characteristic value is then sent to the cloud processor 5 via the data sending device 4. At the cloud processor 5, a classifier 8 is trained by using historical or stored sound data of different fault classifications. Classifier 8 employs a neural network algorithm and adjusts the feature vectors to better represent the feature values of the different fault classifications.

Fig. 6 is a schematic analysis flow chart of the classifier 8. The classifier 8 transmits the received calculated feature values to three neurons, performs analysis calculation for each of the different fault classifications by each neuron, outputs a prediction confidence interval for each fault classification, and outputs an analysis result indicating that fault classification may or may not occur, based on the confidence interval.

Fig. 7 is a flow chart of an escalator monitoring system according to a third embodiment of the present invention.

In the third embodiment, the digital collecting device 1 collects sound data at a contact position between a drive device and a drive chain of an escalator, a contact position between a drive sprocket and a drive chain of an escalator, or a contact position between a step guide block and a skirt panel below a step band cowl. The sound file containing the sound data is transmitted to the cloud processor 5 through the data transmission device 4. At the cloud processor 5, the sound data is denoised by the band pass filter 3 to obtain a specific frequency range [ F [ ]_{Is low in},F_{Height of}]The sound data of the inside. F_{Is low in}Usually about 1000 to 5000Hz, F_{Height of}Typically greater than 10000 Hz. The cloud processor 5 then calculates Key Performance Indicators (KPIs) of the filtered sound data. The KPI associated with noise generated by contact between metal parts may be a Root Mean Square (RMS) value of the sound data. In this embodiment, the RMS value is used to calculate the peak value of the filtered sound data. The cloud processor 5 then compares the RMS value to a predetermined threshold. If the RMS is higher, the cloud processor willResponding by sending an alarm signal to the customer service center, or by communicating directly with maintenance personnel to perform lubrication maintenance. This comparison is exemplarily shown in fig. 3. The predetermined threshold value may be obtained by testing under various lubrication conditions or by other kinds of experiments.

Fig. 8 is a flow chart of an escalator monitoring system according to a fourth embodiment of the present invention.

In the fourth embodiment, the digital collecting device 1 is a sound data collecting device that collects sound data at a contact position between the driving device and the drive chain, at a contact position between a drive sprocket of a gear box of an escalator and the drive chain, or at a contact position between a step guide block and an apron below a step band cowl. The sound file containing the sound data is transmitted to the cloud processor 5 through the data transmission device 4. At the cloud processor 5, a classifier 8 is trained by using historical or stored sound data of different fault classifications. Classifier 8 employs a neural network algorithm and adjusts the feature vectors to better represent the feature values of the different fault classifications.

In this embodiment, the cloud processor 5 calculates a feature value of the sound data. The eigenvalues may be zero-crossing rates, energies, energy entropies, spectral centroids, spectral entropies, spectral fluxes, spectral roll-offs, spectral cepstral coefficients (MFCCs), chrominance vectors, or chrominance offsets.

Fig. 5 shows a comparison between sound data of different fault types. Fig. 5(a) shows a sound data pattern generated when step misalignment occurs, fig. 5(b) shows a sound data pattern generated by a rusted drive chain, and fig. 5(c) shows a sound data pattern when the escalator is in a normal operation state. Obviously, different types of failure sound data have different patterns and therefore different feature values.

Fig. 6 is a schematic analysis flow chart of the classifier 8. The classifier 8 transmits the received calculated feature values to three neurons, each neuron analyzes and calculates for different fault classifications, outputs a prediction confidence interval for each fault classification, and outputs an analysis result according to the confidence interval.

The third and fourth embodiments differ from the first and second embodiments mainly in that data processing in the local device 6 in the first and second embodiments is performed in the cloud processor 5 in the third and fourth embodiments. But this may increase the costs associated with data transfer from the local device 6 to the cloud processor 5.

Fig. 9 is a flow chart of an escalator lubrication condition monitoring system according to a fifth embodiment.

In the fifth embodiment, the digital collecting device 1 collects sound data at a contact position between the driving device and the driving chain, at a contact position between a driving sprocket of a gear box of the escalator and the driving chain, or at a contact position between a step guide block and a skirt panel below a step band front cowl. The sound file containing the sound data is denoised by the band-pass filter 3 to obtain a noise signal in a specific frequency range F_{Is low in},F_{Height of}]The sound data of the inside. F_{Is low in}Usually about 1000 to 5000Hz, F_{Height of}Typically greater than 10000 Hz. Then, the local data processing apparatus 2 calculates a Key Performance Indicator (KPI) of the filtered sound data. The KPI associated with noise generated by contact between metal parts may be a Root Mean Square (RMS) value of the sound data. In this embodiment, the RMS value is used to calculate the peak value of the filtered sound data. In this embodiment, the local data processing apparatus 2 also compares the RMS value with a predetermined threshold. This comparison process is exemplarily shown in fig. 3. If the RMS is higher, the local data processing device 2 issues a response 7, which response 7 may be an alarm signal. And transmitting the alarm signal to a customer service center or directly contacting maintenance personnel to carry out escalator lubrication maintenance. The local data processing apparatus 2 may also send only the RMS value to the cloud processor. The predetermined threshold value may be obtained by testing under various lubrication conditions or by other kinds of experiments.

Fig. 10 is a flow chart of an escalator monitoring system according to a sixth embodiment of the present invention.

In the sixth embodiment, the digital collecting device 1 collects sound data at a contact position between the driving device and the driving chain, at a contact position between a driving sprocket of a gear box of the escalator and the driving chain, or at a contact position between a step guide block and a skirt panel below a step band front cowl. The local data processing apparatus 2 located at the local 6 directly calculates the feature value of the sound data. The classifier 8 is trained by using historical or stored sound data for different fault classifications. Classifier 8 employs a neural network algorithm and adjusts the feature vectors to better represent the feature values of the different fault classifications. In this embodiment, the feature value may be a zero-crossing rate, energy entropy, spectral centroid, spectral entropy, spectral flux, spectral roll-off, spectral cepstral coefficient (MFCC), chroma vector, or chroma bias.

Fig. 5 shows a comparison between sound data of different fault types. Fig. 5(a) shows a sound data pattern generated when step misalignment occurs, fig. 5(b) shows a sound data pattern generated by a rusted drive chain, and fig. 5(c) shows a sound data pattern when the escalator is in a normal operation state. Obviously, different types of failure sound data have different patterns and therefore different feature values.

Fig. 6 is a schematic analysis flow chart of the classifier 8. The classifier 8 transmits the received calculated feature values to three neurons, each neuron analyzes and calculates for different fault classifications, outputs a prediction confidence interval for each fault classification, and outputs an analysis result according to the confidence interval. The local data processing apparatus may respond to the result. The response can be an alarm signal, and the alarm signal can be sent to a customer service center or directly contact maintenance personnel to carry out lubrication maintenance on the escalator. Or the result can be directly sent to the cloud processor.

The escalator monitoring system according to the invention can collect and analyze data in any environment, including indoor or outdoor, quiet and noisy environments, and does not rely on human subjective judgment. The method and the system also predict possible faults without stopping the machine, save maintenance time, reduce related cost and improve the safety and riding comfort of the elevator.

Fig. 11 is a first embodiment of a sound data collection device 1 for use in an escalator monitoring system according to various embodiments of the present invention. In this embodiment, the sound data collection device 1 includes a sensor circuit box 11. The sensor circuit box 11 includes a top wall 12, a bottom wall 13, and side walls 14. A sound pickup hole 15 is defined in the top wall 12, the hole 15 being a through hole. The acoustic data collection device 1 also includes an acoustic sensor 16 disposed within the chamber. The acoustic sensor 16 rests on the bottom wall 13 of the sensor circuit box by means of a bracket 17. At the opening of the sound pick-up aperture towards the chamber, above the sound sensor 16, a water-proof membrane 18 is arranged. The waterproof film prevents water or moisture from entering the sound data collecting device 1 and has good sound transmission performance.

Fig. 12 is a second embodiment of the sound data collection device 1 for an escalator monitoring system according to various embodiments of the present invention. This embodiment is different from the first embodiment in that the sound sensor 16 is attached directly to the top wall 12 of the sensor circuit box 11, below the sound pickup hole 15, and also below the waterproof film 18.

Fig. 13 is a third embodiment of the sound data collection device 1 for an escalator monitoring system according to various embodiments of the present invention. This embodiment is different from the first embodiment in that the sound data collecting apparatus 1 is configured as a two-sided sound data collecting apparatus. The sensor circuit box 11 thereof has sound pickup holes 15 and 19 of a through hole type and two sound sensors 16 and 20 in the top wall 12 and the bottom wall 13, respectively. The acoustic sensor rests on top of the top wall 12 and the bottom wall 13 by means of brackets 17 and 21, respectively. Each sound pick-up aperture 15 and 19 has attached at its opening to the chamber a water-repellent membrane 18 and 22.

Fig. 14 is a fourth embodiment of the sound data collection device 1 for an escalator monitoring system according to various embodiments of the present invention. This embodiment is different from the second embodiment in that the sound data collecting apparatus 1 is configured as a two-sided sound data collecting apparatus. The sensor circuit box 11 thereof has sound pickup holes 15 and 19 of a through hole type and two sound sensors 16 and 20 in the top wall 12 and the bottom wall 13, respectively. Each sound sensor is attached directly to the top wall 12 and the bottom wall 13. Each sound pick-up aperture 15 and 19 has attached at its opening to the chamber a water-repellent membrane 18 and 22. Each waterproofing membrane 18 and 22 is located between the opening of the sound pick-up holes 15 and 19 towards the chamber and the sound sensor attached at the respective opening.

Next, referring to fig. 15 to 17, a flowchart of a sixth embodiment of the escalator monitoring system according to the present invention will be described in detail. This embodiment relates to monitoring the temperature of a handrail of an escalator.

Fig. 15 shows a block diagram of an escalator monitoring system according to the invention. The monitoring system 100 includes a temperature sensor assembly 110, a data transmission unit 4, and a server 140. The temperature sensor assembly 110 is used to detect the ambient temperature and the temperature of the handrail in the stop state, the standby state, and the full speed state of the escalator. The back of the handrail is the surface which is in direct friction contact with the friction wheel, the bearing, the guide wheel or guide block of the handrail and the metal of the curved arc section. For example, the temperature sensor assembly 110 includes an ambient temperature sensor for detecting an ambient temperature and a handrail temperature sensor for detecting a temperature of a back surface of the handrail. Generally, when the escalator is in a stopped state, the ambient temperature is equal to the handrail temperature. Therefore, the ambient temperature and the handrail back temperature when the escalator is in the standby-speed running state and the full-speed running state are of greater concern to technicians who need to know the running state of the escalator. In this embodiment, the temperature sensor assembly 110 senses the ambient temperature and the handrail back temperature when the escalator is in a full speed operating condition. The processor 120 collects the ambient temperature and the handrail back temperature detected by the temperature sensor assembly by wire or wirelessly (e.g., radio frequency, bluetooth, zigbee) and transmits the ambient temperature and the handrail back temperature to the server 130 at a predetermined frequency (e.g., 1 minute 1 time).

The server 130 compares the ambient temperature with the handrail back temperature to obtain a temperature difference between the ambient temperature and the handrail back temperature, and a predetermined temperature difference threshold is stored in the server, and if the temperature difference between the ambient temperature and the handrail back temperature exceeds the predetermined temperature difference threshold, the server sends an alarm signal.

In particular, a serviceThe temperature sensor assembly 110 collects the handrail back temperature and the ambient temperature sensed by the temperature sensor assembly 110 over a predetermined time period t1 and compares the ambient temperature and the handrail back temperature to obtain a first average M of the temperature difference between the ambient temperature and the handrail back temperature over the predetermined time period₁And a first variance value sigma₁. In addition, the server collects the temperature of the temperature sensor assembly 110 over another predetermined period of time t₂The detected ambient temperature is compared with the back temperature of the handrail, and a second average value M of the temperature difference between the back temperature of the handrail and the ambient temperature in the other preset time period is obtained₂And a second variance value sigma₂。

Next, the server compares the second average value with the first average value, and if the difference between the second average value and the first average value is greater than k times (M) the first variance value₂>M₁+kσ₁) A first alarm signal is sent out, wherein k is an integer which is set in the server and is larger than 1.

Further, the server also judges the first variance value sigma₁And a first predetermined threshold T₁And a second variance value sigma₂And a second predetermined threshold T₂If the first variance value is less than a first predetermined threshold and the second variance value is less than a second predetermined threshold, i.e. σ₁<T₁And sigma₂<T₂The local data processing device or the cloud processor will send a second alarm signal. The second alarm signal is more accurate than the first alarm signal.

Fig. 16 and 17 show the escalator in a friction wheel drive mode and an end wheel drive mode, respectively. Fig. 16 shows a friction wheel 101 and a handrail 102. Fig. 17 shows the end wheel 103. In these two different handrail drive modes, the direction of handrail travel is different. As the mounting location of the clamp for the temperature sensor assembly depends on the direction of movement of the handrail, different situations can arise in the mounting of the clamp in the two handrail drive modes, as will be described in more detail below.

Fig. 18-21 show the clip 104 mounted on the C-piece of the panel in the friction wheel drive mode. As shown in fig. 19-20, the clip 104 is mounted near the friction wheel, on the C-piece of the panel. The jig includes a bracket 41 and an adjustable plate 42. The adjustable plate 42 is mounted on the bracket by a connection means such as a bolt and a nut. The bracket 41 is mounted to the C-shaped member of the skirt by bolts and nuts. A temperature sensor assembly 43 is mounted on the end of the adjustable plate 42 remote from the C-shaped member.

One of the adjustable plate 42 and the bracket 41 has a first elongated hole extending in the length direction, and the other of the adjustable plate and the bracket has an opening. A bolt may pass through the first elongated hole and the opening to connect the adjustable plate and the bracket, and a distance a of the temperature sensor assembly from the C-shaped member in the length direction may be adjusted according to an alignment position of the opening with the first elongated hole in the length direction, whereby a handrail center distance L may be changed to accommodate different types of escalators.

Fig. 22-25 show the clamp 40 mounted on the stanchion in an end wheel drive mode. As shown in fig. 22 and 23, the clamp is mounted on the post. The clamp 40 includes a bracket 41 and an adjustable plate 42. The adjustable plate 42 is mounted on the bracket by a connection means such as a bolt and a nut. The bracket 41 is mounted on the pillar by bolts and nuts. A temperature sensor assembly 43 is mounted at an end of the adjustable plate remote from the post.

One of the adjustable plate and the bracket has a second elongated hole 44 extending in the width direction perpendicular to the length direction, and the other of the adjustable plate and the bracket has a third elongated hole 45 extending in the length direction. A bolt may pass through the second elongated hole and the third elongated hole to connect the adjustable plate and the bracket. They also adjust the distance between the temperature sensor assembly and the pillar in the length direction (handrail center distance L) and the distance W between the temperature sensor assembly and the handrail in the width direction, based on the alignment positions of the second elongated hole and the third elongated hole.

Fig. 26 shows a flow chart of a first embodiment of the monitoring method of the escalator according to the invention. At step 100', ambient temperature and handrail temperature in a stop state, a standby state, and a full speed state of the escalator are detected using the temperature sensor assembly. At step 200', the ambient temperature and the handrail back temperature are received from the temperature sensor assembly using the data transfer unit and transferred to the local data processing device or the cloud processor at a predetermined frequency. In step 300', the server compares the ambient temperature with the handrail back temperature to obtain a temperature difference between the ambient temperature and the handrail back temperature, and a predetermined temperature difference threshold is stored in the local data processing device or the cloud processor. If the temperature difference between the ambient temperature and the temperature of the back of the armrest exceeds a predetermined temperature difference threshold, the server will send an alarm signal.

In step 300', the server further collects the handrail back temperature and the ambient temperature detected by the temperature sensor assembly within a predetermined period of time, and compares the ambient temperature and the handrail back temperature to obtain a first average value and a first variance value of temperature differences between the ambient temperature and the handrail back temperature within the predetermined period of time.

In step 300', the server further collects an ambient temperature detected by the temperature sensor assembly during another predetermined period of time and a handrail back temperature, and compares the ambient temperature and the handrail back temperature to obtain a second average value and a second variance value of a temperature difference between the handrail back temperature and the ambient temperature during the another predetermined period of time.

In step 300', the server is configured to issue a first alarm signal when the difference between the second average value and the first average value is greater than k times the first variance value, where k is an integer greater than 1 set in the server.

At step 300', the server is further configured to determine a relationship between the first variance value and a predetermined first threshold value, and a relationship between the second variance value and a predetermined second threshold value, and if the first variance value is less than the predetermined first threshold value and the second variance value is less than the predetermined second threshold value, the server issues a second alarm signal.

The escalator monitoring system can monitor the running condition of the handrail in real time. Possible problems can be known in advance, thus providing a safer escalator. In addition, the real-time monitoring device is suitable for different installation situations of the escalator, and the clamp of the temperature sensor assembly is adjustable. The mounting position of the clamp can be adjusted according to different mounting sizes of various types of escalators

Next, referring to fig. 27 to 31, a flowchart of an eighth embodiment of the escalator monitoring system according to the present invention will be described in detail. This embodiment relates to the monitoring of passenger flow by the escalator monitoring system according to the invention.

Fig. 27 shows a block diagram of an escalator monitoring system according to the present invention. The monitoring system 100 comprises a passenger flow sensor 211, a motor power sensor 212, a data transmission unit 4 and a server 130. The passenger flow sensor 211 is a device installed at an entrance of the escalator for emitting light beams in opposite directions, and the frequency at which the light beams are interrupted reflects the passenger flow entering the escalator. The motor power sensor 212 is configured to detect motor power consumed per unit time. The data transmission unit 4 collects the passenger flow volume and the motor power per unit time and transmits them to the server 130 by means of, for example, 2G, 3G, or 4G. The server 130 calculates a ratio (KPI) of the passenger flow volume per unit time to the motor power per unit time and compares the ratio with a predetermined ratio stored in the server 130. And if the ratio KPI has abnormal mutation relative to the preset ratio, the server predicts the failure time of the escalator and sends a predictive maintenance signal. For example, reference numeral "2" in fig. 28 shows that the ratio of the passenger flow volume per unit time to the motor power per unit time suddenly increases, which indicates that the passenger flow volume at this time is not matched with the motor power consumption, and the escalator malfunctions. It will be understood by those skilled in the art that the traffic volume per time unit is the ratio of the total traffic volume in a certain time to the time, and that the total traffic volume in the time cannot be too small.

Fig. 29 and 30 show two types of mismatch relationships between the passenger flow volume per unit time and the motor power consumption per unit time, respectively. Fig. 29 shows a fault which the escalator is considered to have occurred if the passenger flow sensor does not detect the passenger flow and the motor power consumption detected by the motor power sensor does not match the predetermined power consumption in the case of no passenger flow, and the server issues an alarm signal. Fig. 30 shows that during a period of time exceeding a predetermined threshold, if the passenger flow sensor detects at least some passenger flow while the motor power consumption is running in a standby condition, the escalator suddenly accelerates so that the passenger falls and is injured, and the server issues an alarm signal.

In addition, when there is no passenger flow, the power sensor detects an average power consumption for a predetermined period of time. The server stores a predetermined threshold range. If the average power consumption does not fall within a predetermined threshold range stored in the server, the server generates an alarm signal.

Fig. 31 shows a flow chart of a second embodiment of the monitoring method of the escalator according to the invention.

In step 2100, the amount of passenger entering the elevator per unit time is detected using a passenger flow sensor. In step 2200, motor power consumption per unit time is detected using a power sensor. In step 2300, the detected passenger flow volume per unit time and the detected motor power consumption per unit time are transferred to a server using a data transfer unit. In step 2400, the server determines an operation state of the escalator according to a relationship between a passenger flow amount per unit time and power consumption per unit time to determine whether to transmit an alarm signal.

In addition, in step 2400, the server also calculates a ratio of power consumption in unit time to passenger flow in unit time, and if the ratio has an abnormal sudden change, an alarm signal is sent out; if the ratio does not abnormally change abruptly and tends to increase within a predetermined time and exceeds a predetermined threshold, the server issues an alarm signal.

In addition, in the case where the escalator is in the standby operation state, the power sensor detects an average power for a predetermined period of time in step 2200, and the server transmits an alarm signal if the average power does not fall within a predetermined threshold range stored in the server in step 2400.

At step 2400, the server is further configured to send an alarm signal if the passenger flow volume detected by the passenger flow volume sensor is zero and the power consumption detected by the power sensor does not match the power consumption set for a passenger flow volume of zero for a period of time exceeding a predetermined threshold period of time.

In step 2400, the server is further configured to issue an alarm signal if the traffic detected by the traffic sensor is not zero and the power consumption detected by the power sensor is a standby power output for a period of time exceeding a predetermined threshold period of time.

The real-time monitoring equipment can monitor the relation between passenger flow and power output in real time and know the running state of the escalator, thereby improving the maintenance of the escalator, improving the energy consumption of the escalator and providing the safety of the escalator.

The server described herein may be a local processor along with the data collection device, or a cloud processor capable of remote monitoring. All data processing may be processed only at the local processor, or only at the cloud processor, or as described above with respect to the embodiments, partially at the local processor and then the remainder at the cloud processor. The present invention is incorporated herein in various embodiments based on these concepts and is not limited to those embodiments described above.

The above embodiments are merely exemplary and should not be construed as limiting the present invention. Features from the various embodiments may be combined to implement further embodiments of the invention, the scope of which is determined solely by the appended claims. Various modifications and changes may be made to the described embodiments without departing from the scope of the present invention.