阅读说明：本技术 自动扶梯上的物联网上的传感器融合 (Sensor fusion on internet of things on escalator ) 是由 D·O·帕尔克 P·谢德尔 R·塔卡奇 于 2020-11-13 设计创作，主要内容包括：本发明涉及一种用于自动扶梯的监测系统,其包括：本地网关装置；分析机,其通过云计算网络与本地网关装置通信；乘客检测机,其通过云计算网络与分析机通信,乘客检测机配置成确定自动扶梯的乘客数据；以及感测设备,其通过短程无线协议与本地网关装置进行无线通信,感测设备包括：惯性测量单元传感器,其配置成检测自动扶梯的加速度数据,其中,感测设备、本地网关装置以及分析机中的至少一个配置成响应于至少加速度数据而确定自动扶梯的基于状况的监测(CBM)健康评分,其中,分析机配置成响应于至少乘客数据而调整CBM健康评分。(The invention relates to a monitoring system for an escalator, comprising: a local gateway device; an analyzer in communication with a local gateway device through a cloud computing network; a passenger detection machine in communication with the analyzer through a cloud computing network, the passenger detection machine configured to determine passenger data for the escalator; and a sensing device that wirelessly communicates with the local gateway apparatus by a short-range wireless protocol, the sensing device including: an inertial measurement unit sensor configured to detect acceleration data of the escalator, wherein at least one of the sensing device, the local gateway device, and the analyzer is configured to determine a condition-based monitoring (CBM) health score of the escalator in response to at least the acceleration data, wherein the analyzer is configured to adjust the CBM health score in response to at least the passenger data.)

1. A monitoring system for an escalator, the monitoring system comprising:

a local gateway device;

an analyzer in communication with the local gateway device over a cloud computing network;

a passenger detection machine in communication with the analysis machine over a cloud computing network, the passenger detection machine configured to determine passenger data for the escalator; and

a sensing device in wireless communication with the local gateway apparatus over a short-range wireless protocol, the sensing device comprising:

an inertial measurement unit sensor configured to detect acceleration data of the escalator,

wherein at least one of the sensing device, the local gateway device, and the analysis machine is configured to determine a condition-based monitoring (CBM) health score for the escalator in response to at least the acceleration data,

wherein the analysis machine is configured to adjust the CBM health score in response to at least the passenger data.

2. The monitoring system of claim 1, further comprising:

a microphone configured to detect sound data of the escalator,

wherein the CBM health score is determined in response to at least one of the acceleration data and the sound data.

3. The monitoring system of claim 1, wherein the analyzer is configured to adjust a maintenance schedule of the escalator in response to the passenger data.

4. The monitoring system of claim 1, wherein the passenger detection machine includes at least one of a camera, a light curtain, a load sensor, and an online database.

5. The monitoring system of claim 2, wherein the sensing device is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

6. The monitoring system of claim 2, wherein the sensing device is configured to communicate the acceleration data and the sound data to the local gateway device, and the local gateway device is configured to determine a CBM health score for the escalator in response to at least one of the acceleration data and the sound data.

7. The monitoring system of claim 2, wherein the sensing device is configured to transmit the acceleration data and the sound data to the analysis engine through the local gateway device and the cloud computing network, and wherein the analysis engine is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

8. The monitoring system of claim 1, wherein the sensing device is located within a handrail of the escalator and moves with the handrail.

9. The monitoring system of claim 1, wherein the sensing device is attached to and moves with a step chain of the escalator.

10. The monitoring system of claim 1, wherein the sensing device is stationary and positioned proximate to a step chain of the escalator or a drive machine of the escalator.

11. The monitoring system of claim 1, wherein the sensing device is attached to a moving member of a drive machine of the escalator.

12. The monitoring system of claim 11, wherein the moving member of the drive machine is an output sheave that drives a step chain of the escalator.

13. The monitoring system of claim 2, wherein the sensing device uses at least one of the inertial measurement unit sensor to detect low frequency vibrations less than 10 Hz and the microphone to detect high frequency vibrations greater than 10 Hz.

14. A monitoring system for an escalator, the monitoring system comprising:

a local gateway device;

an analyzer in communication with the local gateway device over a cloud computing network;

a meteorological data source in communication with the analyzer through the cloud computing network, the meteorological data source configured to obtain meteorological data at a location of the escalator; and

a sensing device in wireless communication with the local gateway apparatus over a short-range wireless protocol, the sensing device comprising:

an inertial measurement unit sensor configured to detect acceleration data of the escalator,

wherein at least one of the sensing device, the local gateway device, and the analysis machine is configured to determine a condition-based monitoring (CBM) health score for the escalator in response to at least the acceleration data,

wherein the analysis machine is configured to adjust the CBM health score in response to at least the meteorological data.

15. The system of claim 14, further comprising:

a microphone configured to detect sound data of the escalator,

wherein the CBM health score is determined in response to at least one of the acceleration data and the sound data.

16. The monitoring system of claim 14, wherein the analyzer is configured to adjust a maintenance schedule of the escalator in response to the meteorological data.

17. The monitoring system of claim 15, wherein the sensing device is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

18. The monitoring system of claim 15, wherein the sensing device is configured to communicate the acceleration data and the sound data to the local gateway device, and the local gateway device is configured to determine a CBM health score for the escalator in response to at least one of the acceleration data and the sound data.

19. The monitoring system of claim 15, wherein the sensing device is configured to transmit the acceleration data and the sound data to the analysis engine through the local gateway device and the cloud computing network, and wherein the analysis engine is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

20. A method of monitoring an escalator, the method comprising:

detecting acceleration data of the escalator using an inertial measurement unit sensor located in a sensing device;

determining a condition-based monitoring (CBM) health score for the escalator in response to at least the acceleration data;

obtaining meteorological data at a location of the escalator;

detecting passenger data of the escalator; and

adjusting the CBM health score in response to at least one of the weather data and the passenger data.

Technical Field

Embodiments herein relate to the field of conveying systems, and in particular to methods and apparatus for monitoring a conveying apparatus of a conveying system.

Background

The health of the transport equipment within a transport system, such as, for example, elevator systems, escalator systems, and moving walkways, may be difficult and/or costly to determine.

Disclosure of Invention

According to an embodiment, there is provided a monitoring system for an escalator, the monitoring system comprising: a local gateway device; an analyzer in communication with a local gateway device through a cloud computing network; a passenger detection machine in communication with the analyzer through a cloud computing network, the passenger detection machine configured to determine passenger data for the escalator; and a sensing device that wirelessly communicates with the local gateway apparatus by a short-range wireless protocol, the sensing device including: an inertial measurement unit sensor configured to detect acceleration data of the escalator, wherein at least one of the sensing device, the local gateway device, and the analyzer is configured to determine a condition-based monitoring (CBM) health score of the escalator in response to at least the acceleration data, wherein the analyzer is configured to adjust the CBM health score in response to at least the passenger data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: a microphone configured to detect sound data of the escalator, wherein the CBM health score is determined in response to at least one of the acceleration data and the sound data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the analyzer is configured to adjust a maintenance schedule (schedule) of the escalator in response to the passenger data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the passenger detection machine includes at least one of a camera, a light curtain, a load sensor, and an online database.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is configured to determine a CBM health score of the escalator responsive to at least one of the acceleration data and the acoustic data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is configured to transmit the acceleration data and the sound data to the local gateway device, and the local gateway device is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is configured to transmit the acceleration data and the sound data to the analysis machine through the local gateway device and the cloud computing network, and wherein the analysis machine is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is located within a handrail of the escalator and moves with the handrail.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is attached to a step chain (step chain) of the escalator and moves with the step chain.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is stationary and positioned close to the step chain of the escalator or the drive machine of the escalator.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is attached to a moving member of a drive machine of the escalator.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the moving member of the drive machine is an output pulley that drives the step chain of the escalator.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device uses at least one of an inertial measurement unit sensor to detect low frequency vibrations less than 10 Hz and a microphone to detect high frequency vibrations greater than 10 Hz.

According to another embodiment, a monitoring system for an escalator is provided. The monitoring system includes: a local gateway device; an analyzer in communication with a local gateway device through a cloud computing network; a meteorological data source in communication with the analyzer through the cloud computing network, the meteorological data source configured to obtain meteorological data at a location of the escalator; and a sensing device that wirelessly communicates with the local gateway apparatus by a short-range wireless protocol, the sensing device including: an inertial measurement unit sensor configured to detect acceleration data of the escalator, wherein at least one of the sensing device, the local gateway device, and the analysis machine is configured to determine a condition-based monitoring (CBM) health score of the escalator in response to at least the acceleration data, wherein the analysis machine is configured to adjust the CBM health score in response to at least the meteorological data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: a microphone configured to detect sound data of the escalator, wherein the CBM health score is determined in response to at least one of the acceleration data and the sound data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the analyzer is configured to adjust a maintenance schedule of the escalator in response to the meteorological data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is configured to determine a CBM health score of the escalator responsive to at least one of the acceleration data and the acoustic data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is configured to transmit the acceleration data and the sound data to the local gateway device, and the local gateway device is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

In addition or as an alternative to one or more of the features described herein, further embodiments may include: the sensing device is configured to transmit the acceleration data and the sound data to the analysis machine through the local gateway device and the cloud computing network, and wherein the analysis machine is configured to determine a CBM health score of the escalator in response to at least one of the acceleration data and the sound data.

According to another embodiment, there is provided a method of monitoring an escalator, the method comprising: detecting acceleration data of the escalator using inertial measurement unit sensors located in the sensing device; determining a condition-based monitoring (CBM) health score for the escalator in response to at least the acceleration data; obtaining meteorological data at a location of an escalator; detecting passenger data of the escalator; and adjusting the CBM health score in response to at least one of the weather data and the passenger data.

Technical effects of embodiments of the present disclosure include monitoring health of an escalator system using at least one of acceleration data, meteorological data, and passenger data.

The foregoing features and elements may be combined in various combinations, which are not exclusive, unless expressly indicated otherwise. These features and elements, as well as their operation, will become more apparent in light of the following description and the accompanying drawings. It is to be understood, however, that the description and drawings are intended to be illustrative and explanatory in nature, and not restrictive.

Drawings

The present disclosure is illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.

Fig. 1 is a schematic illustration of an escalator system and a monitoring system according to an embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a sensing device of the monitoring system of FIG. 1, according to an embodiment of the present disclosure; and

fig. 3 is a flow chart of a method of monitoring an escalator according to an embodiment of the present disclosure.

Detailed Description

Figure 1 illustrates an escalator 10. It should become apparent in the description that follows that the present invention is applicable to other passenger conveyor systems, such as moving walkways. Escalator 10 generally includes a truss 12 extending between a lower landing 14 and an upper landing 16. A plurality of successively connected steps or treads 18 are connected to a step chain 20 and travel through a closed loop path within truss 12. The pair of balustrades 22 includes moving handrails 24. The drive machine 26 or drive system is typically located in a machine space 28 below the upper landing 16; however, additional machine space 28' can be located below the lower landing 14. The drive machine 26 is configured to drive the tread plates 18 and/or the handrail 24 through the step chain 20. The drive machine 26 is operated under normal operating conditions to move the pedals 18 in the selected direction at a desired speed.

In a turnaround area 19 located below the lower landing 14 and the upper landing 16, the pedal 18 makes a 180 degree change in the direction of travel. The tread 18 is pivotally attached to the step chain 20 and follows the closed loop path of the step chain 20 running from one landing to another and back again.

The drive machine 26 includes a first drive component 32, such as a motor output pulley, the first drive component 32 being connected to a drive motor 34 by a belt reduction assembly 36, the belt reduction assembly 36 including a second drive component 38, such as an output pulley, the second drive component 38 being driven by a tensioning component 39, such as an output belt. The first drive member 32 in some embodiments is a drive member and the second drive member 38 is a driven member.

As used herein, in various embodiments, the first drive component 32 and/or the second drive component may be any type of rotating device, such as a pulley, a gear, a wheel, a sprocket, a cog wheel, a pinion, and the like. In various embodiments, the tensioning member 39 can be configured as a chain, belt, cable, thin belt, strap, bar, or any other similar device that operatively connects two elements to provide a driving force from one element to the other. For example, the tensioning member 39 may be any type of interconnecting member that extends between the first and second drive members 32, 38 and operatively connects the first and second drive members 32, 38. In some embodiments, as shown in fig. 1, the first drive component 32 and the second drive component may provide belt deceleration. For example, the first drive member 32 may be about 75 mm (2.95 inches) in diameter, while the second drive member 38 may be about 750 mm (29.53 inches) in diameter. Belt deceleration, for example, allows for replacement of the pulleys to change speeds for 50 or 60 Hz power supply power applications or different step speeds. However, in other embodiments, the second drive component 38 may be substantially similar to the first drive component 32.

As noted, the first drive member 32 is driven by the drive motor 34 and is thus configured to drive the tensioning member 39 and the second drive member 38. In some embodiments, the second drive member 38 may be an idler gear or similar device driven by operatively connecting between the first drive member 32 and the second drive member 38 by way of the tensioning member 39. The tensioning member 39 travels around a loop set by the first drive member 32 and the second drive member 38, which loop may be referred to as a small loop hereinafter. A small loop is provided to drive the larger loop of step chain 20 and is driven by, for example, an output pulley 40. Under normal operating conditions, the tension members 39 and the step chain 20 move in unison based on the speed of movement of the first drive member 32 when driven by the drive motor 34.

The escalator 10 also includes a controller 115 in electronic communication with the drive motor 34. As shown, the controller 115 can be located in the machine space 28 of the escalator 10 and configured to control operation of the escalator 10. For example, the controller 115 may provide drive signals to the drive motor 34 to control acceleration, deceleration, stopping, etc. of the tread plates 18 via the step chain 20. The controller 115 may be an electronic controller as follows: including a processor and associated memory, the memory including computer-executable instructions that, when executed by the processor, cause the processor to perform various operations. The processor may be, but is not limited to, a single processor or a multi-processor system of any of a wide variety of possible architectures including a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), or Graphics Processing Unit (GPU) hardware in a homogeneous or heterogeneous arrangement. The memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), or any other electronic, optical, magnetic, or any other computer readable medium.

Although described herein as a particular escalator drive system and particular components, this is merely exemplary and those skilled in the art will appreciate that other escalator system configurations may operate with the invention disclosed herein.

The elements and components of the escalator 10 may suffer fatigue, wear and tear or other damage that detracts from the health of the escalator 10. The embodiments disclosed herein seek to provide a monitoring system 200 for the escalator 10 of fig. 1.

In accordance with an embodiment of the present disclosure, a monitoring system 200 is illustrated in fig. 1. The monitoring system 200 includes one or more sensing devices 210, the sensing devices 210 configured to detect sensor data 202 of the escalator 10, process the sensor data 202, and transmit the processed sensor data 202a (e.g., a condition-based monitoring (CBM) health score 318) to a cloud-connected analyzer 280. Alternatively, the sensor data 202 may be originally sent to at least one of the local gateway device 240 and the analysis engine 280, where the raw sensor data 202b will be processed. The processed sensor data 202a may simply be the CBM health score 318. Based on the type of sensor data 202 collected by the sensing devices 210, the analysis engine 280 can select, via the network 250, how the sensor data 202 from different sensing devices 210 cooperate with each other.

The raw sensor data 202b and/or the processed sensor data 202a may be communicated in data packets 207 between the local gateway device 240 and the network 250. The data packet 207 may be transmitted using a secure internet protocol (e.g., UDP, TCP) using payload and message encryption (e.g., AES 256). Data packets 207 may be transmitted in an efficient manner at a selected frequency. For example, the data packet 207 may be transmitted every two minutes to establish an uninterrupted connection using either a dynamic IP address or a static IP address. Data packet 207 may be transmitted using data compression (e.g., MQTT) to have two-way communication with network 250. Information such as, for example, heartbeat data, remote (intervention) commands, over-the-air updates to firmware, etc. may be communicated. The heartbeat data may include information about the status of the escalator 10. The intervention command may comprise a command that can be sent to the device to change the operation of the device. In an embodiment, the data compression is MQTT data compression. The compression of the sensor data 202 does not affect the manner in which the sensor data 202 interacts with other sensor data 202. The processed sensor data 202a and the raw sensor data 202b may be available via the network 250.

The sensor data 202 may include, but is not limited to, pressure data 314, vibration characteristics (i.e., vibration over a period of time) or acceleration data 312, and sound data 316. The acceleration data 312 may be a derivative or integral of the acceleration data 312 of the escalator 10, such as, for example, position distance, velocity, jerk (jerk), jerk (jounce), jerk (snap) … …, and so forth. The sensor data 202 may also include light, humidity, and temperature data or any other desired data parameter. It should be appreciated that while particular systems are defined separately in the schematic block diagrams, each or any of the systems may additionally be combined or separated via hardware and/or software. For example, the sensing device 210 may be a single sensor or may be a plurality of individual sensors.

The monitoring system 200 can include one or more sensing devices 210 located in various locations of the escalator 10. In one example, the sensing device 210 may be positioned to be attached to the armrest 24 or positioned within the armrest 24 and move with the armrest 24. In another example, the sensing device 210 is stationary and positioned proximate to the drive machine 26 or the step chain 20. In another example, the sensing device 210 can be attached to the step chain 20 and move with the moving step chain 20. In another example, the sensing device 210 may be attached to the pedal 18 and move with the pedal 18. In another example, the sensing device 210 may be attached to the drive machine 26 and move relative to the moving step chain 20. In another embodiment, sensing device 210 may be attached to a moving member of drive machine 26. The moving member of the drive machine 26 can be an output sheave 40 that drives the step chain 20 of the escalator 10. The cloud computing network 250 makes it possible to automatically select the sensing device 210 based on the desired result displayed on the computing apparatus 400. This can avoid having the same sensing device 210 in all escalators 10. Thus, algorithms in the cloud computing network 250 may be able to cooperate in such a way that the processed sensor data 202a or raw sensor data 202b from the sensing devices 210 that are available create a selected result or return a "message unavailable". For example, the acceleration data 312 (i.e., vibration data) and the sound data 316 may cooperate in such a way that if multiple sensing devices 210 find a fault, the fault is only displayed on the computing device. A "message unavailable" will be sent in the absence of sensor data 202 (e.g., vibration/acceleration data 312).

In an embodiment, the sensing device 210 is configured to process the sensor data 202 by a processing method (such as, for example, edge processing) prior to transmitting the sensor data 202 to the analyzer 280. Advantageously, utilizing edge processing helps to conserve power by reducing the amount of data that needs to be transferred. In another embodiment, the sensing device 210 is configured to transmit raw and unprocessed raw sensor data 202b to the analyzer 280 for processing.

Processing of the sensor data 202 may reveal data such as: such as vibration, vibration characteristics, sound, temperature, acceleration of the escalator 10, deceleration of the escalator, escalator ride performance, emergency stops, and the like.

The analysis machine 280 may be a computing device such as, for example, a desktop computer, a cloud-based computer, and/or a cloud-based Artificial Intelligence (AI) computing system. The analysis machine 280 may also be a computing device typically carried by a person, such as, for example, a smart phone, a PDA, a smart watch, a tablet computer, a laptop computer, and so forth. The analysis engine 280 may also be two separate devices that are synchronized together, such as a cell phone and a desktop computer that are synchronized through an internet connection, for example.

The analysis machine 280 may be an electronic controller that includes a processor 282 and associated memory 284, the memory 284 including computer-executable instructions that, when executed by the processor 282, cause the processor 282 to perform various operations. The processor 282 may be, but is not limited to, a single processor or a multi-processor system of any of a wide variety of possible architectures including a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), or Graphics Processing Unit (GPU) hardware in a homogeneous or heterogeneous arrangement. The memory 284 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), or any other electronic, optical, magnetic, or any other computer readable medium.

The sensing device 210 is configured to transmit the raw sensor data 202b or the processed sensor data 202a to the local gateway apparatus 240 via the short-range wireless protocol 203. The short-range wireless protocol 203 may include, but is not limited to, Bluetooth, BLE, Wi-Fi, LoRa, insignnu, enOcean, Sigfox, HaLow (801.11ah), zWave, ZigBee, wireless M-Bus, or other short-range wireless protocols known to those skilled in the art. In an embodiment, the local gateway device 240 may utilize message queue telemetry transport (MQTT or MQTT SN) to communicate with the sensing devices 210. Advantageously, MQTT minimizes network bandwidth and device resource requirements, which helps reduce power consumption in the local gateway device 240 and sensing devices 210, while helping to ensure reliability and message delivery. Where a short-range wireless protocol 203 is used, the sensing device 210 is configured to transmit the raw or processed sensor data 202 directly to the local gateway apparatus 240, and the local gateway apparatus 240 is configured to transmit the raw or processed sensor data 202 to the analysis engine 280 or to the controller 115 over the network 250. Network 250 may be a computing network such as: such as a cloud computing network, a cellular network, or any other computing network known to those skilled in the art. Where a long-range wireless protocol 204 is used, the sensing device 210 is configured to transmit the sensor data 202 to the analyzer 280 over the network 250. The long-range wireless protocols 204 may include, but are not limited to, cellular, 3G, 4G, 5G, Cat M1, Weightless (i.e., P narrowband Internet of things protocol), LTE (NB-IoT, CAT M1), LoRa, Satellite, Ingeniu, or SigFox. The local gateway device 240 may use the short-range wireless protocol 203 to communicate with the controller 115 through a hard-wired connection and/or a wireless connection.

The sensing device 210 may be configured to detect sensor data 202 including acceleration in any number of directions. In an embodiment, the sensing device 210 may detect the sensor data 202 including acceleration data 312 along three axes (X, Y, and Z axes). As illustrated in fig. 1, the X-axis and the Y-axis may form a plane parallel to the pedal 18, and the Z-axis is perpendicular to the pedal 18. The Z-axis is parallel to the vertical or gravitational direction. X is parallel to the horizontal movement of the pedal 18 and the Y axis is perpendicular to the horizontal movement of the pedal 18.

In fig. 1, a computing device 400 is also shown. The computing device 400 can be part of an escalator mechanic/technician working on the escalator 10 or monitoring the escalator 10. Computing device 400 may be a computing device such as a desktop computer or a mobile computing device, such as, for example, a smart phone, a PDA, a smart watch, a tablet computer, a laptop computer, and so forth, typically carried by a person. The computing device 400 can include a display device so that the mechanic can visually see the sensor data 202 or CBM health score 318 of the escalator 10. As shown in fig. 1, computing device 400 may include a processor 420, a memory 410, a communication module 430, and an application 440. The processor 420 can be a computer processor, such as any type or combination of a microprocessor, microcontroller, digital signal processor, application specific integrated circuit, programmable logic device, and/or field programmable gate array. Memory 410 is an example of a non-transitory computer-readable storage medium tangibly embodied in computing device 400, including executable instructions stored in computing device 400, e.g., as firmware. The communication module 430 may implement one or more communication protocols, such as, for example, a short-range wireless protocol 203 and a long-range wireless protocol 204. The communication module 430 may be in communication with at least one of the controller 115, the sensing device 210, the network 250, and the analyzer 280. In an embodiment, the communication module 430 may communicate with the analysis engine 280 over the network 250.

Communication module 430 is configured to receive CBM health score 318 and/or sensor data 202 from network 250 and analysis engine 280. Application 440 is configured to generate a graphical user interface on computing device 400 to display CBM health score 318. The application 440 may be computer software (e.g., as a service) that is installed directly on the memory 410 of the computing device 400 and/or that is installed remotely and accessible through the computing device 400.

In fig. 1, a meteorological data source 700 is also shown, the meteorological data source 700 configured to provide meteorological data 710 to at least one of the controller 115, the analyzer 280, and the computing device 400 of the escalator 10. The meteorological data source 700 may be in wireless electronic communication with at least one of the controller 115, the analyzer 280, and the computing device 400 of the escalator 10 via the network 250. The meteorological data source 700 may be in wireless electronic communication with the network 250 via the long-range wireless protocol 204. The weather data source may be one or more weather stations that detect weather data 710, and/or the weather data source 700 may be an online weather database, such as, for example, a national weather service or a mid-European weather forecast center. The weather data 710 may include weather conditions at the location of the escalator 10, including past weather conditions, present weather conditions, and future weather conditions, such as, for example, rain, snow, sleet, temperature, wind, fog, humidity, visibility, pressure, dew point, lightning, air quality, and so forth. The meteorological data 710 may help explain why the escalator 10 is operating poorly, if it is just snowing and dirt/debris is being tracked into the tread 18, or if rain is running into the escalator 10, forcing the escalator 10 to shut down. The analyzer 280 is configured to adjust the CBM health score 318 based on the meteorological data 710.

Additionally, the analyzer 280 can be configured to command the controller 155 of the escalator 10 to adjust operation of the escalator 10 in response to the weather data 710. For example, if a flood is predicted to occur in the location of the escalator 10 from the meteorological data 710, the escalator 10 can be shut down prior to the flood to protect the escalator 10.

In fig. 1, a passenger detection machine 800 is also shown, the passenger detection machine 800 configured to transmit passenger data 810 to at least one of the controller 115, the analyzer 280, and the computing device 400 of the escalator 10. Passenger data 810 may include an approximate number of passengers currently riding an escalator or an approximate number of passengers that will ride an escalator in the future. The passenger detection machine 800 can be in wireless electronic communication with at least one of the controller 115, the analyzer 280, and the computing device 400 of the escalator 10 via the network 250. The passenger detection machine 800 may be in wireless electronic communication with the network 250 via the long-range wireless protocol 204. The passenger detection machine 800 can be a camera 822, a light curtain 824, a load sensor 826 located at the floor at the upper or lower landing 16, 14, and/or an online database 828.

The camera 822 may be a video camera, 2D camera, 3D camera, thermal imager, infrared camera, or similar camera known to those skilled in the art that utilizes image recognition (e.g., neural network) for people counting. The cameras 822 may be positioned close to the escalator 10 or in an area remote from the escalator 10. For example, the camera 822 may be located at an exit of a train or a sports field where a substantial number of exiting individuals may be expected to use the escalator 10. The online database 828 may be an online calendar that may indicate when passengers may be using the escalator 10. The online schedule may be the following online schedule of a train or a stadium: it is possible to indicate when an individual may be leaving a train or a sports field and using the escalator 10. When this passenger data 810 is reported to the analyzer 280, the analyzer 280 is better able to predict future use of the escalator 10, which may affect the operation of the escalator 10 and the maintenance schedule 850 of the escalator 10. The maintenance schedule 850 can be a schedule of scheduled maintenance for the escalator 10 and can be stored in the memory 284 of the analyzer 280.

Light curtain 824 may be a human detection device as follows: a light beam is projected and it is detected when the light beam is passed by an individual in order to count the number of individuals that pass through the light beam. The light curtain 824 can be positioned proximate to the escalator 10 (e.g., near an entrance of the escalator 10 at a lower landing 14 or an upper landing 16) or in an area remote from the escalator 10. For example, the light curtain 824 may be located at an exit of a train or a sports arena where a substantial number of exiting individuals may be expected to use the escalator 10. When this passenger data 810 is reported to the analyzer 280, the analyzer 280 is better able to predict future use of the escalator 10, which may impact the operation and maintenance schedule 850. For example, if one of the online database 828, the light curtain 824, and the camera 822 detects that a motion event at a playing field has just ended, the analyzer 280 may direct the controller 115 of the escalator 10 to adjust the direction of movement of the tread plates 18 to ensure that passengers are moved away from the playing area.

Load sensor 826 may be a separate load sensor as follows: the load on the floor near the lower landing 14 or the upper landing 16 is measured to determine how many passengers are riding the escalator 10. Alternatively, the load sensor 826 may use the controller 115 of the escalator 10 to determine the load on the tread. For example, the load on the pedal 18 may be determined by detecting a change in the load on the drive machine 26. A change in the load on the drive machine 26 can be correlated to a change in the load on the pedal 18.

The load on the drive machine 26 can be determined by detecting a change in the grid current required by the drive machine 26 to operate the escalator at a specified speed. An increase in the load on the pedal 18 will require the drive machine 26 to work harder to move the pedal 18, and thus the current from the grid source to the drive machine 26 will increase. The increase in current may be proportional to the number of passengers utilizing escalator 10, and thus the number of passengers may be determined by the increase in current.

In addition, the load on the drive machine 26 can be determined by placing strain gauges on the bearings of the second drive component 38, and by measuring the displacement of the strain gauges, the escalator load can be verified.

The detection of passenger data 810 depicting passenger usage of the escalator 10 can have an impact on wear and aging of the escalator 10 and the individual components of the escalator 10. Thus, the analyzer 280 can be configured to adjust the maintenance schedule 350 of the escalator 10 in response to the passenger data 810. For example, if the escalator 10 is being utilized by more passengers than usual at a higher than average rate, scheduled maintenance may need to be performed more frequently than usual. In one example, CBM health score 318 may be adjusted in response to passenger data 810, which then adjusts maintenance schedule 850.

In addition, the detection of meteorological data 710 that depicts the operating conditions of escalator 10 may have an impact on the wear and aging of escalator 10 and the individual components of escalator 10. Thus, the analyzer 280 can be configured to adjust the maintenance schedule 350 of the escalator 10 in response to the meteorological data 710. For example, if the escalator 10 is being utilized above an average temperature than usual, scheduled maintenance may need to be performed more frequently than usual, as an increase in temperature may have an impact on the oil performance within the escalator 10. In one example, the CBM health score 318 may be adjusted in response to the meteorological data 710, which then adjusts the maintenance schedule 850.

Fig. 2 illustrates a block diagram of the sensing device 210 of the monitoring system 200 of fig. 1. It should be appreciated that while particular systems are defined separately in the schematic block diagram of fig. 2, each or any of the systems may additionally be combined or separated via hardware and/or software. As shown in fig. 2, the sensing device 210 may include a controller 212, a plurality of sensors 217 in communication with the controller 212, a communication module 220 in communication with the controller 212, and a power source 222 electrically connected to the controller 212.

The plurality of sensors 217 includes an Inertial Measurement Unit (IMU) sensor 218, the Inertial Measurement Unit (IMU) sensor 218 configured to detect the sensor data 202 including the sensing device 210 and the acceleration data 312 of the escalator 10. The IMU sensor 218 may be a sensor such as: such as an accelerometer, gyroscope, or similar sensor known to those skilled in the art. The acceleration data 312 detected by the IMU sensor 218 may include acceleration as well as derivatives or integrals of acceleration, such as, for example, velocity, jerk … …, and so forth. The IMU sensor 218 is in communication with the controller 212 of the sensing device 210.

The plurality of sensors 217 includes a pressure sensor 228, the pressure sensor 228 configured to detect sensor data 202 including pressure data 314 (such as, for example, atmospheric air pressure proximate the escalator 10). In two non-limiting examples, the pressure sensor 228 may be a pressure altimeter or a barometer. The pressure sensor 228 is in communication with the controller 212.

The plurality of sensors 217 includes a microphone 230, the microphone 230 configured to detect sensor data 202 including sound data 316, such as, for example, audible sound and sound levels. The microphone 230 may be a 2D (e.g., stereo) or 3D microphone. The microphone 230 is in communication with the controller 212.

The plurality of sensors 217 may also include additional sensors including, but not limited to, a light sensor 226, a pressure sensor 228, a humidity sensor 232, and a temperature sensor 234. The light sensor 226 is configured to detect sensor data 202 including an exposure amount. The light sensor 226 is in communication with the controller 212. The humidity sensor 232 is configured to detect sensor data 202 including a humidity level. The humidity sensor 232 is in communication with the controller 212. The temperature sensor 234 is configured to detect the sensor data 202 including a temperature level. The temperature sensor 234 is in communication with the controller 212.

The plurality of sensors 217 of the sensing device 210 can be utilized to determine various operating modes of the escalator 10. Any of a plurality of sensors 217 can be utilized to determine that the escalator 10 is operating. For example, the microphone 230 may detect a characteristic noise indicating that the escalator 10 is operating, or the IMU sensor 218 may detect a characteristic acceleration indicating that the escalator 10 is operating. The pressure sensor 228 may be utilized to determine the operating speed of the escalator 10. For example, if the sensing device 210 is located on the step chain 20 or the tread plate 18, a continuous or constant air pressure change may indicate movement of the step chain 20, and thus, the operating speed may be determined in response to the change in air pressure. The IMU sensor 218 may be utilized to determine the height of the escalator 10. For example, if the sensing device 210 is located on the handrail 24 or the tread plate 18, a change in direction of speed (e.g., a step moving upward and then a sudden downward movement) may indicate that the handrail 24 or the tread plate 18 has reached a maximum height. The IMU sensor 218 may be utilized to determine the stopping distance of the escalator 10. For example, if the sensing device 210 is located on the handrail 24, the step chain 20, or the tread plate 18, a second integral of the deceleration of the sensing device 210 may be calculated to determine the braking distance. The stopping distance may be determined from the acceleration data 312 indicating an acceleration (the integrated velocity of the measured vibration from the acceleration data 312) above a threshold for the first zero crossing of the filtered sensor data. The IMU sensor 218 may be utilized to determine the occupancy status of the escalator 10. For example, if the sensing device 210 is located on the step chain 20 or the tread plate 18, the vibrations detected by the sensing device 210 using the IMU sensor 218 may indicate the entry of a passenger onto the escalator 10 or the exit of a passenger from the escalator 10.

The controller 212 of the sensing device 210 includes a processor 214 and associated memory 216, the processor 214 and associated memory 216 including computer-executable instructions that, when executed by the processor 214, cause the processor 214 to perform various operations, such as, for example, edge preprocessing or processing of sensor data 202 collected by the IMU sensor 218, the light sensor 226, the pressure sensor 228, the microphone 230, the humidity sensor 232, and the temperature sensor 234. In an embodiment, the controller 212 may process the acceleration data 312 and/or the pressure data 314 to determine the altitude of the sensing device 210 if the sensing device 210 is located on a member that is raised or lowered during operation of the escalator 10 (such as, for example, the handrail 24 and step chain 20). In an embodiment, the controller 212 of the sensing device 210 may process the sensor data 202 using a Fast Fourier Transform (FFT) algorithm.

The processor 214 may be, but is not limited to, a single processor or a multi-processor system of any of a wide variety of possible architectures including a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), or Graphics Processing Unit (GPU) hardware in a homogeneous or heterogeneous arrangement. The memory 216 may be a storage device such as, for example, a Random Access Memory (RAM), a Read Only Memory (ROM), or any other electronic, optical, magnetic, or any other computer readable medium.

The power supply 222 of the sensing device 210 is configured to store electrical power and supply electrical power to the sensing device 210. The power source 222 may include an energy storage system such as, for example, a battery system, a capacitor, or other energy storage systems known to those skilled in the art. The power supply 222 may also generate electrical power for the sensing device 210. The power source 222 may also include an energy generation or power harvesting system such as, for example, a synchronous generator, an induction generator, or other types of power generators known to those skilled in the art. The power source 222 may also be a hard-wired power supply that is hard-wired to and receives power from the power grid and/or escalator 10.

The sensing device 210 includes a communication module 220, the communication module 220 configured to allow the controller 212 of the sensing device 210 to communicate with the local gateway apparatus 240 via the short-range wireless protocol 203. The communication module 220 may be configured to communicate with the local gateway device 240 using a short-range wireless protocol 203, such as, for example, bluetooth, BLE, Wi-Fi, LoRa, insigninu, enOcean, Sigfox, HaLow (801.11ah), zWave, ZigBee, wireless M-Bus, or other short-range wireless protocols known to those skilled in the art. As described above, where the short-range wireless protocol 203 is used, the communication module 220 is configured to transmit the sensor data 202 to the local gateway device 240, and the local gateway device 240 is configured to transmit the sensor data 202 to the analyzer 280 over the network 250.

The communication module 220 may also allow the sensing device 210 to communicate with other sensing devices 210 directly through the short-range wireless protocol 203 or indirectly through the local gateway apparatus 240 and/or the cloud computing network 250. Advantageously, this allows the sensing device 210 to coordinate the detection of the sensor data 202.

The sensing device 210 includes an altitude determination module 330, the altitude determination module 330 configured to determine an altitude or (i.e., height) of the sensing device 210 located on a moving component of the escalator 10, such as, for example, the tread plates 18, step chains 20, and/or handrails 24. The altitude determination module 330 may utilize various approaches to determine the altitude or (i.e., height) of the sensing device 210. The altitude determination module 330 may be configured to determine the altitude of the sensing device 210 using at least one of the pressure altitude determination module 310 and the acceleration altitude determination module 320.

The acceleration altitude determination module 320 is configured to determine a change in height of the sensing device in response to the acceleration of the sensing device 210 detected along the Z-axis. The sensing device 210 can detect acceleration along the Z-axis shown at 322, and can integrate the acceleration at 324 to derive a vertical velocity of the sensing device. At 326, the sensing device 210 may also integrate the vertical velocity of the sensing device 210 to determine the vertical distance traveled by the sensing device 210 during the detection of the acceleration data 312 at 322. The direction of travel of the sensing device 210 may also be determined in response to the detected acceleration data 312. The altitude determination module 330 may then determine the altitude of the sensing device 210 in response to the starting altitude and the distance traveled away from the starting altitude. The starting altitude may be based on past operation and/or movement of the tracking sensing device 210. An abnormal change in acceleration and/or speed of the escalator may indicate a poor CBM health score 318.

The pressure altitude determination module 310 is configured to detect atmospheric air pressure using the pressure sensor 228 when the sensing device is in motion and/or stationary. In two non-limiting embodiments, the pressure detected by pressure sensor 228 may be associated with altitude by a look-up table or an operation on altitude using barometric pressure changes. The direction of travel of the sensing device 210 may also be determined in response to changes in pressure detected via the pressure data 314. For example, a change in pressure may indicate that the sensing device 210 is moving upward or downward. The pressure sensor 228 may need to periodically detect the baseline pressure to account for changes in barometric pressure due to local meteorological conditions. For example, in non-limiting embodiments, it may be desirable to detect the baseline pressure daily, hourly, or weekly. In some embodiments, the baseline pressure may be detected every time the sensing device is stationary or at regular intervals when the sensing device 210 is stationary and/or at a known altitude. It may also be desirable to detect acceleration of the sensing device 210 to know when the sensing device 210 is stationary, and then, when the sensing device 210 is stationary, the sensing device 210 may need to be offset to compensate for sensor drift and environmental drift.

In one embodiment, the pressure altitude determination module 310 may be used to verify and/or correct the altitude of the sensing device 210 determined by the acceleration altitude determination module 320. In another embodiment, the acceleration altitude determination module 320 may be used to verify and/or correct the altitude of the sensing device determined by the pressure altitude determination module 310. In another embodiment, the pressure altitude determination module 310 may be prompted to determine the altitude of the sensing device 210 in response to acceleration detected by the IMU sensor 218.

The health determination module 311 is configured to determine a CBM health score 318 for the escalator 10. The CBM health score 318 can be associated with a particular component of the escalator 10, or be the CBM health score 318 for all of the escalators 10. The health determination module 311 may be located in the analysis engine 280, the local gateway device 240, or the sensing device 210. In an embodiment, the health determination module 311 is located in the sensing device 210 to perform edge processing. Health determination module 311 may process sensor data 202 using an FFT algorithm to determine CBM health score 318. In one embodiment, the health determination module 311 may process at least one of the sound data 316 detected by the microphone 230, the light detected by the light sensor 226, the humidity detected by the humidity sensor 232, the temperature data detected by the temperature sensor 234, the acceleration data 312 detected by the IMU sensor 218, and/or the pressure data 314 detected by the pressure sensor 228 to determine a CBM health score 318 for the escalator 10.

In an embodiment, the health determination module 311 may process at least one of the sound data 316 detected by the microphone 230 and the acceleration data 312 detected by the IMU sensor 218 to determine a CBM health score 318 for the escalator 10.

Different frequency ranges may be required to detect different types of vibrations in the escalator 10, and different sensors of the sensing device 210 (e.g., microphone, IMU sensor 218 … …, etc.) may be better suited to detect different frequency ranges. In one example, the vibrations in the handrail 24 can consist of low frequency component vibrations of less than 5hz and higher frequency vibrations induced at points where friction in the handrail 24 is likely to occur. Low frequency vibrations may be best detected using the IMU sensor 218, while higher frequency vibrations (e.g., in the kHz range) may be best detected using the microphone 230, which is more power efficient. Advantageously, it is more energy efficient to use the microphone to detect higher frequency vibrations and the IMU sensor 218 to detect lower frequency vibrations. In an embodiment, the higher frequency may include a frequency greater than or equal to 10 Hz. In an embodiment, the lower frequency may comprise a frequency less than or equal to 10 Hz.

The sensing device 210 may be placed in a particular position to capture vibrations from different components. In an embodiment, the sensing device 210 may be placed in the handrail 24 (i.e., move with the handrail 24). When positioned in the armrest 24, the sensing device 210 may capture low frequency vibrations with the IMU sensor 218. Any change in low frequency vibration from baseline may indicate a low CBM health score 318. Foreign objects (e.g., dirt, dust, pebbles) may become trapped in the armrest 24, thereby causing increased vibration. In one example, low frequency oscillations may occur due to dust or dirt causing friction. These low frequency oscillations can be identified using a low pass filter of less than 2 Hz. In another example, a single spike or noise may occur due to dirt sticking to the rails or wheels of the step chain 20. These single spikes or noise can be detected by identifying spikes greater than 100 mg in vibration.

In an embodiment, the sensing device 210 may be attached to (e.g., attached in or on) the step chain 20 or tread plate 18 (i.e., move with the step chain 20 or tread plate 18). In another embodiment, sensing device 210 is positioned in stationary proximity to drive machine 26. When the sensing device 210 is attached to the drive machine 26, the temperature sensor 234 may optimally measure the temperature of the drive machine 26. The IMU sensor 218 may optimally measure acceleration when the sensing device 210 is attached to the output pulley 40. When attached to the step chain 20 or positioned in stationary proximity to the drive machine 26, the sensing device 210 may utilize the IMU sensor 218 to capture low frequency vibrations that may indicate bearing issues with respect to the main pivot of the step chain 20, the step rollers of the step chain 20, or the handrail pivot of the handrail 24. Alternatively, the sensing device 210 may utilize the microphone 230 to capture high frequency vibrations that may indicate bearing problems when attached to the step chain 20 or positioned in stationary proximity to the drive machine 26. An FFT algorithm may be utilized to help analyze the high frequency vibrations captured by the microphone. Advantageously, the FFT algorithm uses predefined special electronic hardware, resulting in an easy, low cost and low power consuming way of detecting the deviation. When attached to the step chain 20 or positioned in stationary proximity to the drive machine 26, the sensing device 210 may measure temperature with the temperature sensor 234. An elevated temperature may indicate an increased load or increased friction on the machine driven machine 26. When attached to the step chain 20, the sensing device 210 may utilize the IMU sensors 218 to capture acceleration in multiple axes (e.g., X, Y, and Z axes) to determine the tread plate 18 direction (e.g., up or down); a 3D acceleration profile of the pedal 18 to determine (among other things) when the pedal 18 is turning; the step chain 18 misalignment and bumps in the step chain 20 that may indicate foreign objects (dirt, pebbles, dirt … …, etc.) in the step chain 20 or the step chain 18. The combination of multiple sensor information from different sensors of the multiple sensors 217 results in the ability for the sensors within the sensing device to fuse, thus allowing the sensors to determine, adjust, or reject data reads altogether. For example, an increase in acceleration values within acceleration data 312 (at certain frequencies (FFT)) may be associated with an increase in temperature detected by temperature sensor 234 (e.g., machine heat driving machine 26 due to higher loads) and an increase in relative humidity detected by humidity sensor 232 (excluding changes in friction due to external meteorological conditions).

The CBM health score 318 can be a graded grade indicating the health of the escalator 10 and/or components of the escalator 10. In a non-limiting example, CBM health score 318 may be ranked on a one to ten scale, where CBM health score 318 equals one of the lowest CBM health scores 318, and CBM health score 318 equals ten is the highest CBM health score 318. In another non-limiting example, CBM health score 318 may be ranked on a one percent to one hundred percent scale, where CBM health score 318 equals one percent is the lowest CBM health score 318, and CBM health score 318 equals one hundred percent is the highest CBM health score 318. In another non-limiting example, CBM health score 318 may be graded by a hierarchy of colors, with CBM health score 318 being the lowest CBM health score 318 in red and CBM health score 318 being the highest CBM health score 318 in green. The CBM health score 318 may be determined in response to at least one of the acceleration data 312, the pressure data 314, and/or the temperature data. For example, acceleration data 312 above a threshold acceleration (e.g., normal operating acceleration) on any of the X-axis, Y-axis, and Z-axis may indicate a low CBM health score 318. In another example, increased temperature data above a threshold temperature for the component may indicate a low CBM health score 318. In another example, elevated sound data 316 above a threshold sound level for the component may indicate a low CBM health score 318.

Reference is now made to fig. 3, along with the components of fig. 1-2. Fig. 3 shows a flow chart of a method 500 of monitoring an escalator, according to an embodiment of the present disclosure. In an embodiment, the method 500 may be carried out by at least one of the sensing device 210, the local gateway apparatus 240, the application 440, and the analysis engine 280.

At block 504, the acceleration data 312 of the escalator 10 is detected using the IMU sensor 218 located in the sensing device 210. In one embodiment, the sensing device 210 is located within the handrail 24 of the escalator 10 and moves with the handrail 24. In another embodiment, the sensing device 210 is attached to the step chain 20 of the escalator 10 and moves with the step chain 20. In another embodiment, the sensing device 210 is attached to the tread plate 18 of the escalator 10 and moves with the tread plate 18. In another embodiment, the sensing device 210 is stationary and positioned proximate to the step chain 20 of the escalator 10 or the drive machine 26 of the escalator 10. At block 506, sound data 316 of the escalator 10 is detected using a microphone 230 positioned in the sensing device 210.

At block 508, a CBM health score 318 is determined in response to at least one of the acceleration data 312 and the sound data 316. Alternatively, the CBM health score 318 may be determined in response to at least the acceleration data 312. At block 510, meteorological data 710 at the location of the escalator 10 is obtained. The meteorological data 710 may be obtained from a meteorological data source 700. At block 512, passenger data 810 for the escalator 10 is detected by the passenger detector 800. The passenger detector 800 may include at least one of a camera 822, a light curtain 824, and a load sensor 826.

At block 514, the CBM health score 318 is adjusted in response to at least one of the weather data 710 and the passenger data 810. The maintenance schedule 850 may also be adjusted in response to at least one of the weather data 710 and the passenger data 810. The maintenance schedule 850 may be adjusted directly or indirectly through at least one of the meteorological data 710 and the passenger data 810. For example, the maintenance schedule 850 may be adjusted indirectly through at least one of the weather data 710 and the passenger data 810 because the CBM health score 318 is adjusted directly through at least one of the weather data 710 and the passenger data 810. Thus, a change in the CBM health score 318 can adjust the maintenance schedule 850 for the escalator 10 in the future. The method 500 can also include adjusting operation of the escalator in response to at least one of the weather data 710 and the passenger data 810. For example, operation of the escalator 10 can be slowed down during extreme heat or shut down during or before flooding.

The method 500 may further include: a CBM health score 318 for the escalator 10 is determined in response to at least one of the acceleration data 312 and the sound data 316. In one embodiment, the sensing device 210 is configured to determine a CBM health score 318 for the escalator 10 in response to at least one of the acceleration data 312 and the sound data 316.

In another embodiment, the acceleration data 312 and the sound data 316 are communicated to a local gateway device 240 in wireless communication with the sensing device 210 via the short-range wireless protocol 203, and the local gateway device 240 is configured to determine a CBM health score 318 for the escalator 10 in response to at least one of the acceleration data 312 and the sound data 316.

In another embodiment, the acceleration data 312 and the sound data 316 are transmitted to a local gateway apparatus 240 in wireless communication with the sensing device 210 over the short-range wireless protocol 203, and the local gateway apparatus 240 transmits the acceleration data 312 and the sound data 316 to the analyzer 280 over the cloud computing network 250. The analyzer 280 is configured to determine a CBM health score 318 for the escalator 10 in response to at least one of the acceleration data 312 and the sound data 316.

In an embodiment, low frequency vibrations less than 10 Hz are detected using the IMU sensor 218. In another embodiment, higher frequency vibrations greater than 10 Hz use the microphone 230. In another embodiment, the dither is between 10 Hz and 1 kHz. In another embodiment, the dither is greater than 1 kHz.

While the above description has described the flow process of fig. 3 in a particular order, it should be appreciated that the ordering of the steps may be altered unless specifically claimed otherwise in the appended claims.

As described above, embodiments can take the form of processes implemented by a processor and an apparatus (such as a processor) for practicing those processes. Embodiments can also take the form of computer program code (e.g., a computer program product) embodying instructions embodied in tangible media (e.g., non-transitory computer-readable media), such as floppy diskettes, CD ROMs, hard drives, or any other non-transitory computer-readable medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the embodiments. Embodiments can also be in the form of computer program code (e.g., whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation), wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.

The term "about" is intended to include a degree of error associated with measurement based on a particular quantity and/or manufacturing tolerance of equipment available at the time of filing the present application.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof.

Those skilled in the art will recognize that various exemplary embodiments are shown and described herein, each having certain features that are characteristic of the particular embodiments, but the disclosure is not so limited. Rather, the disclosure can be modified to incorporate any number of variations, alterations, substitutions, combinations, sub-combinations or equivalent arrangements not heretofore described, but which are commensurate with the scope of the disclosure. Additionally, while various embodiments of the disclosure have been described, it is to be understood that aspects of the disclosure may include only some of the described embodiments. Accordingly, the disclosure is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.