Intelligent electromagnetic flowmeter system based on Internet of things and fault analysis method
阅读说明：本技术 一种基于物联网的智能电磁流量计系统及故障分析方法 (Intelligent electromagnetic flowmeter system based on Internet of things and fault analysis method ) 是由 马文静 张慧 胡志刚 于 2020-08-03 设计创作，主要内容包括：本发明公开了一种基于物联网的智能电磁流量计系统及故障分析方法,包括电源模块、电磁流量传感器、电磁流量转换控制单元和物联网模块,所述电磁流量转换控制单元和分别与所述电源模块、电磁流量传感器、物联网模块连接；所述电磁流量传感器把被测流体的流量信号转换成相应的标准电压信号,并将该标准电压信号传输至电磁流量转换控制单元进行处理,处理后的数据通过物联网模块与PC机进行相互通讯。本发明采用单片机和CPLD的双控制器模式实现流体流速的精确测量,不仅具有精度高、适应性强、数据实时性高等优点,而且本发明系统利用物联网功能,能够实现电磁流量计的远程监测管理,并对电磁流量计的故障信息进行了合理有效的分析,提高了可靠性分析效率。(The invention discloses an intelligent electromagnetic flowmeter system based on the Internet of things and a fault analysis method, wherein the intelligent electromagnetic flowmeter system comprises a power module, an electromagnetic flow sensor, an electromagnetic flow conversion control unit and an Internet of things module, wherein the electromagnetic flow conversion control unit is respectively connected with the power module, the electromagnetic flow sensor and the Internet of things module; the electromagnetic flow sensor converts the flow signal of the measured fluid into a corresponding standard voltage signal, transmits the standard voltage signal to the electromagnetic flow conversion control unit for processing, and the processed data are communicated with the PC through the Internet of things module. The system disclosed by the invention realizes accurate measurement of the fluid flow rate by adopting a double-controller mode of the single chip microcomputer and the CPLD, has the advantages of high precision, strong adaptability, high data real-time property and the like, and can realize remote monitoring management of the electromagnetic flowmeter by utilizing the function of the Internet of things, reasonably and effectively analyze the fault information of the electromagnetic flowmeter and improve the reliability analysis efficiency.)
1. The utility model provides an intelligence electromagnetic flow meter system based on thing networking which characterized in that: the electromagnetic flow conversion control unit is connected with the power module, the electromagnetic flow sensor and the Internet of things module respectively; the electromagnetic flow sensor converts the flow signal of the measured fluid into a corresponding standard voltage signal, transmits the standard voltage signal to the electromagnetic flow conversion control unit for processing, and the processed data are communicated with the PC through the Internet of things module;
the electromagnetic flow conversion control unit comprises a controller MCU, a CPLD, an A/D converter, a signal processing module, an excitation circuit and an LCD display module, wherein the controller MCU is responsible for completing the control functions of the whole system, including excitation circuit control, sampling control, signal processing function, flow calculation and LCD display control; the input end of the controller MCU is connected with an A/D converter, a power supply module and a flow direction detection module, and the controller MCU is also connected with a CPLD, an LCD display module and an Internet of things module;
the CPLD is responsible for realizing all digital logic circuits required by the whole system, the A/D converter converts external analog signals into digital signals for the analysis and processing of the controller MCU, the signal processing module amplifies and filters voltage and current signals output by the electromagnetic flow sensor, and after the influence of noise and interference is eliminated or weakened, the signals are sampled by the A/D converter and sent to the controller MCU for analysis and processing;
the excitation circuit determines the working magnetic field of the sensor and is responsible for providing excitation current for the transmitter; the LCD display module is used for displaying instantaneous flow, accumulated flow and related instrument parameters in real time.
2. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: the power module outputs 24V alternating current voltage, the 24V alternating current voltage is connected to a BCP56 chip after passing through a rectifying circuit formed by four diodes, the BCP56 chip is connected with an XTR115 chip, 5V direct current voltage is output through the XTR115 chip, the 5V direct current voltage is connected with an input pin of a 65ZY chip, and 3.3V direct current voltage is output through the 65ZY chip.
3. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: the electromagnetic flow sensor conditions the acquired signals through a pre-amplification circuit, a second-order low-pass filter circuit and a high-gain amplification circuit, performs AD conversion processing on the signals, performs data processing on the signals through a controller and a CPLD, calculates flow engineering quantity values, outputs the flow engineering quantity values on an LCD display module, and uploads related detection parameters and calculation results to an upper computer through a wireless transmission module.
4. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: the 5V reference voltage of the a/D converter is provided by an external circuit separately from the 5V voltage of the digital part.
5. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: the Internet of things module comprises a wireless transmission module, a terminal node and a serial port, wherein the terminal node is mainly responsible for collecting flow, temperature and pressure information.
6. The intelligent electromagnetic flowmeter system based on the internet of things of claim 5, characterized in that: the wireless transmission module adopts a Zigbee wireless communication module, and the Zigbee wireless communication module adopts a CC2430 module.
7. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: the controller MCU is also connected with a data storage module and used for storing relevant data and parameters including instantaneous flow and accumulated flow in the electromagnetic flowmeter.
8. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: and the controller MCU selects an LPC2132 singlechip.
9. The intelligent electromagnetic flowmeter system based on the internet of things of claim 1, characterized in that: the CPLD adopts an LC4128V chip.
10. The fault analysis method of the intelligent electromagnetic flowmeter system based on the internet of things as claimed in any one of claims 1-9, wherein: the method comprises the following steps:
performing qualitative analysis on the faults of the electromagnetic flowmeter according to a pre-established fault tree model of the electromagnetic flowmeter to obtain a minimum cut set of the faults of the electromagnetic flowmeter;
acquiring basic fault events of the electromagnetic flowmeter, wherein the basic fault events comprise wiring problems, sealing problems, main board hardware faults, coil problems and electromagnetic flow sensor problems;
establishing a fault distribution function model by referring to a minimal cut set of a fault tree model according to the fault basic event;
calculating the top event probability and the basic event importance of the fault of the electromagnetic flowmeter according to the fault distribution function model of the electromagnetic flowmeter;
wherein, the top event probability of the electromagnetic flowmeter fault is calculated according to the following formula:
wherein y represents the top event probability of the fault of the electromagnetic flowmeter, n represents the minimum cut set number, j is the minimum cut set serial number, and j is 1,2, …, n; i is an element of GjIndicating that the ith basic event belongs to the jth minimal cut set; fiCumulative fault distribution function, F, representing the ith primary eventi＝1-e-λtλ represents the failure probability of the ith basic event, and t represents the time in the time series;
calculating the importance I of the ith basic event of the electromagnetic flowmeter according to the following formulai：
Wherein the content of the first and second substances,
Technical Field
The invention belongs to the technical field of Internet of things, and particularly relates to an intelligent electromagnetic flowmeter system based on the Internet of things and a fault analysis method.
Background
The flow detection instrument and the system are widely applied to technical departments such as cement, chemical engineering, light textile, medicine, paper making, water supply and drainage, food and beverage, and the like, and along with the development of the Internet of things and the improvement of industrial productivity level, the aspects put forward higher and higher requirements on the automatic detection instrument, and the performance, quality and reliability of the product directly influence the economic benefit of enterprises. However, the accompanying sewage discharge problem has become an important task for relevant government functional departments, and a flow meter system for metering sewage discharge is an indispensable tool for quantitatively managing the sewage discharge of enterprises. The electromagnetic flowmeter is a speed type flowmeter for measuring the flow speed of fluid, is an instrument for measuring the volume of conductive fluid, which is manufactured by utilizing the Faraday electromagnetic induction principle, and provides a better method for accurately measuring the flow and the flow speed of the conductive fluid in a pipeline. Compared with various existing non-electromagnetic flow detecting instruments, the electromagnetic flow meter has good performance and wide application range, and is one of the most widely applied flow instruments at present. Most of the existing electromagnetic flowmeters are in a traditional mode, passively receive signals detected by a sensor and converted signals, easily introduce interference signals, have weak signal processing capacity, and process data by using a single controller, so that the existing electromagnetic flowmeters have low detection precision and low reliability, have high maintenance and debugging difficulty, and cannot monitor and manage the running state of the flowmeter and the detected data in real time. At present, the research on the fault diagnosis of the electromagnetic flowmeter in the prior art is not complete, and therefore, finding a fault analysis method for the electromagnetic flowmeter is an urgent problem to be solved.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the defects in the prior art, and provides a high-precision intelligent electromagnetic flowmeter which is strong in practicability and capable of conducting accurate measurement.
The technical scheme is as follows: the intelligent electromagnetic flowmeter system based on the Internet of things comprises a power module, an electromagnetic flow sensor, an electromagnetic flow conversion control unit and an Internet of things module, wherein the electromagnetic flow conversion control unit is respectively connected with the power module, the electromagnetic flow sensor and the Internet of things module; the electromagnetic flow sensor converts the flow signal of the measured fluid into a corresponding standard voltage signal, transmits the standard voltage signal to the electromagnetic flow conversion control unit for processing, and the processed data are communicated with the PC through the Internet of things module;
the electromagnetic flow conversion control unit comprises a controller MCU, a CPLD, an A/D converter, a signal processing module, an excitation circuit and an LCD display module, wherein the controller MCU is responsible for completing the control functions of the whole system, including excitation circuit control, sampling control, signal processing function, flow calculation and LCD display control; the input end of the controller MCU is connected with an A/D converter, a power supply module and a flow direction detection module, and the controller MCU is also connected with a CPLD, an LCD display module and an Internet of things module;
the CPLD is responsible for realizing all digital logic circuits required by the whole system, the A/D converter converts external analog signals into digital signals for the analysis and processing of the controller MCU, the signal processing module amplifies and filters voltage and current signals output by the electromagnetic flow sensor, and after the influence of noise and interference is eliminated or weakened, the signals are sampled by the A/D converter and sent to the controller MCU for analysis and processing;
the excitation circuit determines the working magnetic field of the sensor and is responsible for providing excitation current for the transmitter; the LCD display module is used for displaying instantaneous flow, accumulated flow and related instrument parameters in real time.
Furthermore, the power module outputs an alternating current 24V voltage, the alternating current 24V voltage is connected to a BCP56 chip after passing through a rectifying circuit composed of four diodes, the BCP56 chip is connected with an XTR115 chip, a direct current 5V voltage is output through the XTR115 chip, the direct current 5V voltage is connected with an input pin of a 65ZY chip, and a direct current 3.3V voltage is output through the 65ZY chip.
Furthermore, the electromagnetic flow sensor conditions the acquired signals through a pre-amplification circuit, a second-order low-pass filter circuit and a high-gain amplification circuit, performs AD conversion processing on the signals, performs data processing on the signals through a controller and a CPLD, calculates the flow engineering quantity value, outputs the flow engineering quantity value on an LCD display module, and uploads related detection parameters and calculation results to an upper computer through a wireless transmission module.
Further, the 5V reference voltage of the a/D converter is provided separately from the 5V voltage of the digital part by an external circuit.
Further, the internet of things module comprises a wireless transmission module, a terminal node and a serial port, wherein the terminal node is mainly responsible for collecting flow, temperature and pressure information.
Further, the wireless transmission module adopts a Zigbee wireless communication module, and the Zigbee wireless communication module adopts a CC2430 module.
Furthermore, the controller MCU is also connected with a data storage module for storing relevant data and parameters including instantaneous flow and accumulated flow in the electromagnetic flowmeter.
Further, the controller MCU selects an LPC2132 singlechip.
Further, the CPLD adopts an LC4128V chip.
The invention also discloses a fault analysis method of the intelligent electromagnetic flowmeter system based on the Internet of things, which comprises the following steps:
performing qualitative analysis on the faults of the electromagnetic flowmeter according to a pre-established fault tree model of the electromagnetic flowmeter to obtain a minimum cut set of the faults of the electromagnetic flowmeter;
acquiring basic fault events of the electromagnetic flowmeter, wherein the basic fault events comprise wiring problems, sealing problems, main board hardware faults, coil problems and electromagnetic flow sensor problems;
establishing a fault distribution function model by referring to a minimal cut set of a fault tree model according to the fault basic event;
calculating the top event probability and the basic event importance of the fault of the electromagnetic flowmeter according to the fault distribution function model of the electromagnetic flowmeter;
wherein, the top event probability of the electromagnetic flowmeter fault is calculated according to the following formula:
wherein y represents the top event probability of the fault of the electromagnetic flowmeter, n represents the minimum cut set number, j is the minimum cut set serial number, and j is 1,2, …, n; i is an element of GjIndicating that the ith basic event belongs to the jth minimal cut set; fiCumulative fault distribution function, F, representing the ith primary eventi＝1-e-λtλ represents the failure probability of the ith basic event, and t represents the time in the time series;
calculating the importance I of the ith basic event of the electromagnetic flowmeter according to the following formulai：
Wherein the content of the first and second substances,representing the sum of the probabilities of occurrence of the minimal cut sets containing the ith base event.
Has the advantages that: the invention adopts a double-controller mode of the single chip microcomputer and the CPLD to realize the accurate measurement of the flow velocity of the fluid, and has the advantages of high precision, strong adaptability, high data real-time property and the like compared with the traditional single-controller mode of the flowmeter in an industrial system. The existing electromagnetic flowmeter measurement alarm device is close to the site, and the system provided by the invention can realize remote monitoring and management of the electromagnetic flowmeter by utilizing the function of the Internet of things. The invention not only has some basic functions of the traditional electromagnetic flowmeter, but also has intellectualization, integration, reliability and higher operation processing capability. The provided fault analysis method for the electromagnetic flowmeter provides an effective basis for fault diagnosis of the electromagnetic flowmeter and provides a new reference for links such as design, production and the like of the electromagnetic flowmeter.
Drawings
FIG. 1 is a block diagram of the overall electrical circuit configuration of an electromagnetic flow meter system of the present invention;
FIG. 2 is a framework diagram of the overall design principle of the Internet of things module of the present invention;
FIG. 3 is a flow chart diagram of a fault analysis method for an electromagnetic flow meter system;
fig. 4 is a schematic diagram of an excitation circuit of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1 and 2, an intelligent electromagnetic flowmeter system based on the internet of things comprises a power module, an electromagnetic flow sensor, an electromagnetic flow conversion control unit and an internet of things module, wherein the electromagnetic flow conversion control unit is respectively connected with the power module, the electromagnetic flow sensor and the internet of things module; the electromagnetic flow sensor converts the flow signal of the measured fluid into a corresponding standard voltage signal, transmits the standard voltage signal to the electromagnetic flow conversion control unit for processing, and the processed data are communicated with the PC through the Internet of things module.
The electromagnetic flow conversion control unit comprises a controller MCU, a CPLD, an A/D converter, a signal processing module, an excitation circuit and an LCD display module, wherein the controller MCU is responsible for completing the control functions of the whole system, including excitation circuit control, sampling control, signal processing function, flow calculation and LCD display control; the input end of the controller MCU is connected with an A/D converter, a power supply module and a flow direction detection module, and the controller MCU is further connected with a CPLD, an LCD display module and an Internet of things module.
The CPLD is responsible for realizing all digital logic circuits required by the whole system, the A/D converter converts external analog signals into digital signals for the analysis and processing of the controller MCU, the signal processing module amplifies and filters voltage and current signals output by the electromagnetic flow sensor, and after the influence of noise and interference is eliminated or weakened, the signals are sampled by the A/D converter and sent to the controller MCU for analysis and processing.
The excitation circuit determines the working magnetic field of the sensor and is responsible for providing excitation current for the transmitter; the LCD display module is used for displaying instantaneous flow, accumulated flow and related instrument parameters in real time.
The excitation circuit specifically comprises a pulse width modulation module, a filtering conversion module, an excitation module and a controller; the pulse width modulation module is electrically connected with the filtering conversion module and provides a pulse width modulation wave signal for the filtering conversion module; the filtering conversion module filters the pulse width modulation wave signal and converts the pulse width modulation wave signal into a direct current signal; the filter conversion module is electrically connected with the excitation module, the excitation module comprises an excitation coil, and the excitation coil generates a forward excitation magnetic field or a reverse excitation magnetic field under a direct current signal; the controller is respectively electrically connected with the excitation module and the pulse width modulation module, collects the direct current signal of the excitation coil and controls the pulse width modulation module to adjust the duty ratio of the output pulse width modulation wave signal according to the direct current signal of the excitation coil.
The excitation circuit further comprises an amplifying module, the amplifying module is electrically connected with the filtering conversion module and the excitation module respectively, and the amplifying module is used for amplifying the direct current signals.
The specific excitation circuit structure is as shown in fig. 4 and 4, the filtering and converting module includes a first resistor R1, a first capacitor C1 and a first amplifier U1A, one end of the first resistor R1 is connected to the pulse width modulation module, the other end of the first resistor R1 is connected to one end of the first capacitor C1 and the non-inverting input end of the first amplifier U1A, the other end of the first capacitor C1 is grounded, and the inverting input end of the first amplifier U1A is connected to the output end and to the amplifying module.
The first capacitor C1 is grounded for filtering the pwm wave, and the first amplifier U1A may convert the pwm wave to output a dc signal having a positive correlation with the duty ratio.
With continued reference to fig. 4, the amplifying module includes a second resistor R2, a third resistor R3, and a second amplifier U1B, a non-inverting input terminal of the second amplifier U1B is connected to an output terminal of the first amplifier U1A, an inverting input terminal of the second amplifier U1B is connected to one ends of the second resistor R2 and the third resistor R3, the other end of the second resistor R2 is grounded, and the other end of the third resistor R3 is connected to an output terminal of the second amplifier U1B and the
The amplifying module amplifies the direct current signal output by the filtering conversion module, specifically, the amplification ratio of the amplifying module is related to the second resistor R2 and the third resistor R3, and according to the virtual short and virtual break principle of the amplifier, the amplification ratio of the second amplifier U1B can be obtainedOptionally, the ratio of the resistance of the third resistor R3 to the resistance of the second resistor R2 is less than or equal to 9. At this time, the amplification ratio of the second amplifier U1B is less than or equal to 10, and correspondingly, the range of the dc voltage signal amplified by the amplification module can be expanded to 0-30V, thereby meeting the voltage requirement of the excitation module.
With continued reference to fig. 4, the excitation module includes a fourth resistor R4, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a third amplifier U2A, a fourth amplifier U2B and an excitation coil, wherein the non-inverting input terminal of the third amplifier U2A is connected to the output terminal of the second amplifier U1B, the output terminal of the third amplifier U2A is connected to the inverting input terminal, one end of the fourth resistor R4 and one end of the fifth resistor R5, the other end of the fourth resistor R4 is connected to one end of the excitation coil, the non-inverting input terminal of the fourth amplifier U2B is connected to the seventh resistor R7 and the eighth resistor R8, the other end of the seventh resistor R7 is connected to the power supply, the other end of the eighth resistor R8 is grounded, the output terminal of the fourth amplifier U2B is connected to one end of the sixth resistor R6 and the other end of the excitation coil, and the inverting input terminal of the fourth amplifier U2B is connected to the other end of the sixth resistor R6 and the other end of the fifth resistor R5.
As shown in fig. 4, the voltage difference between two ends of the exciting coil in the exciting module determines the forward and reverse directions and the magnitude of the exciting magnetic field generated by the exciting coil, wherein the voltage signal of one end of the exciting coil is provided by the power source Vs, the seventh resistor R7, the eighth resistor R8, the fourth amplifier U2B and the sixth resistor R6, and the voltage signal of the other end of the exciting coil is determined by the amplified dc signal of the amplifying module, wherein the voltage at the positive-phase input end of the fourth amplifier U2B is Vs · R7/(R7+ R8), and the voltage at the negative-phase input end of the fourth amplifier U2B is Vs · R7/(R7+ R8) according to the virtual short virtual break principle; the voltages of the positive and negative phase input terminals and the output terminal of the third amplifier U2A are equal, the voltage at the positive phase input terminal of the third amplifier U2A is Vin, at this time, the voltage at the end point 1 is Vin, and at this time, in the circuit formed by the fifth resistor R5 and the sixth resistor R6, the voltage at the
In the excitation module, an H-bridge circuit is not required to be arranged, forward and reverse excitation of the excitation coil can be realized only by adjusting the duty ratio of a pulse width modulation signal output by the pulse width modulation module, the structure is simpler, and the size is relatively smaller.
Continuing to refer to fig. 4, the controller includes at least two input ends, the at least two input ends of the controller are respectively connected to two ends of the fourth resistor R4, the controller collects the voltage value of the fourth resistor R4 in real time and sends a pulse width modulation duty cycle adjustment signal to the pulse width modulation module, so as to control the voltage difference value at two ends of the fourth resistor R4 to be a preset voltage difference value.
The fourth resistor R4 may be used as a sampling resistor, and the controller may determine the voltage at the two ends of the excitation coil according to the ratio of the fourth resistor R4 to the impedance of the excitation coil by sampling the voltage at the two ends of the fourth resistor R4, so that the duty ratio of the pulse width modulation signal may be changed in real time to adjust the voltage value at the two ends of the excitation coil to a preset voltage difference value, that is, to obtain the optimal voltage value of the excitation coil, thereby ensuring the lowest loss in the whole circuit. The fourth resistor R4 is only used as a sampling resistor, so that the resistance value cannot be too large, and optionally, the resistance value of the fourth resistor R4 is less than or equal to 1 Ω, so that the loss can be ensured to be low, and the loss of the whole excitation circuit is prevented from increasing.
In this embodiment, the power module outputs an ac 24V voltage, the ac 24V voltage is connected to a BCP56 chip through a rectifying circuit composed of four diodes, the BCP56 chip is connected to an XTR115 chip, a dc 5V voltage is output through the XTR115 chip, the dc 5V voltage is connected to an input pin of a 65ZY chip, and a dc 3.3V voltage is output through the 65ZY chip.
In this embodiment, the electromagnetic flow sensor conditions the acquired signal through the preamplifier circuit, the second-order low-pass filter circuit and the high-gain amplifier circuit, performs AD conversion processing, performs data processing through the controller and the CPLD, calculates a flow engineering quantity value, outputs the flow engineering quantity value on the LCD display module, and uploads the related detection parameters and the calculation result to the upper computer through the wireless transmission module.
In this embodiment, the 5V reference voltage of the a/D converter is provided by an external circuit separately from the 5V voltage of the digital part to ensure high accuracy and stability of the a/D conversion.
In this embodiment, the internet of things module includes wireless transmission module, terminal node, serial port communication, terminal node is mainly responsible for gathering flow, temperature and pressure information.
In this embodiment, the wireless transmission module adopts a Zigbee wireless communication module, and the Zigbee wireless communication module adopts a CC2430 module.
In this embodiment, the controller MCU is further connected to a data storage module for storing relevant data and parameters including instantaneous flow and accumulated flow in the electromagnetic flowmeter.
In this embodiment, the controller MCU is a LPC2132 single chip microcomputer.
In this embodiment, the CPLD uses an LC4128V chip.
As shown in fig. 3, the invention also discloses a fault analysis method of the intelligent electromagnetic flowmeter system based on the internet of things, which comprises the following steps:
performing qualitative analysis on the faults of the electromagnetic flowmeter according to a pre-established fault tree model of the electromagnetic flowmeter to obtain a minimum cut set of the faults of the electromagnetic flowmeter;
acquiring basic fault events of the electromagnetic flowmeter, wherein the basic fault events comprise wiring problems, sealing problems, main board hardware faults, coil problems and electromagnetic flow sensor problems;
establishing a fault distribution function model by referring to a minimal cut set of a fault tree model according to the fault basic event;
calculating the top event probability and the basic event importance of the fault of the electromagnetic flowmeter according to the fault distribution function model of the electromagnetic flowmeter;
wherein, the top event probability of the electromagnetic flowmeter fault is calculated according to the following formula:
wherein y represents the top event probability of the fault of the electromagnetic flowmeter, n represents the minimum cut set number, j is the minimum cut set serial number, and j is 1,2, …, n; i is an element of GjIndicating that the ith basic event belongs to the jth minimal cut set; fiCumulative fault distribution function, F, representing the ith primary eventi＝1-e-λtλ represents the failure probability of the ith basic event, and t represents the time in the time series; calculating the importance I of the ith basic event of the electromagnetic flowmeter according to the following formulai：
Wherein the content of the first and second substances,
The invention adopts a double-controller mode of the single chip microcomputer and the CPLD to realize the accurate measurement of the flow velocity of the fluid, and has the advantages of high precision, strong adaptability, high data real-time property and the like compared with the traditional single-controller mode of the flowmeter in an industrial system. The existing electromagnetic flowmeter measurement alarm device is close to the site, and the system provided by the invention can realize remote monitoring and management of the electromagnetic flowmeter by utilizing the function of the Internet of things. The invention not only has some basic functions of the traditional electromagnetic flowmeter, but also has intellectualization, integration, reliability and higher operation processing capability. The provided fault analysis method for the electromagnetic flowmeter provides an effective basis for fault diagnosis of the electromagnetic flowmeter and provides a new reference for links such as design, production and the like of the electromagnetic flowmeter.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
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