Intelligent elevator operation condition diagnosis system based on fusion diagnosis technology

Abstract

The invention relates to the field of elevator fault diagnosis, and particularly discloses an elevator operation state intelligent diagnosis system based on a fusion diagnosis technology, which comprises a data acquisition module, a signal processing module, a communication module, a cloud server, an intelligent diagnosis module, a memory and a user display terminal, wherein the data acquisition module acquires current, current excitation and main shaft rotating speed signals of a power supply end of a driving motor of the elevator, the signal processing module receives and filters the signals, and the communication module transmits the processed signals to the cloud server; the intelligent diagnosis module diagnoses the state of the motor by analyzing the signal and stores the data, and the user display terminal can call and visually display the state data. Therefore, the problems that an existing elevator fault detection method is complex in operation and inaccurate in test result, and the operation reliability and safety of elevator equipment are affected are solved.

Description

Technical Field

[0001] This patent relates to the field of elevator fault diagnosis, and in particular to an intelligent diagnosis system for elevator operation status based on fusion diagnosis technology. Background Art

[0002] As a key large-scale fixed electromechanical equipment in the coal mine production system, the mine hoisting system is responsible for lifting coal, lifting production equipment, and transporting personnel. The mine hoist is the "throat" equipment of the coal mine, which directly affects the production capacity and economic benefits of coal mining enterprises.

[0003] In the field of hoist fault diagnosis, my country started late in the research of related technologies. Although many related technologies are available now, they are limited by technology. For example, the diagnostic results of vibration method, temperature method, sound wave method and current method are greatly affected by the working environment, and the fault detection content is not comprehensive. The diagnostic results are difficult to interpret and require professional personnel to analyze and judge them, and cannot be presented to customers intuitively. Therefore, with the help of or relying on advanced sensor technology, dynamic testing technology and computer signal processing technology, analyzing the abnormal parts and causes in the equipment, and accurately predicting and diagnosing hidden faults at an early stage are the key to ensuring the safe operation of the hoisting system. Summary of the invention

[0004] In order to solve the problem that the existing elevator fault detection results are greatly affected by the working environment, the fault detection content is not comprehensive, the diagnosis results are difficult to interpret, and there are problems that affect the reliability and safety of the elevator equipment operation.

[0005] The embodiment of the present invention proposes an intelligent diagnosis system for the operation status of a hoist based on fusion diagnosis technology, including: a data acquisition module, a signal hardware processing module, a communication module, a cloud server, an intelligent diagnosis module, a memory and a user display terminal, wherein the data acquisition module and the communication module are respectively connected to the signal hardware processing module, the cloud server includes a memory and an intelligent diagnosis module, and the communication module and the user display terminal are respectively connected to the cloud server in communication; wherein,

[0006] The data acquisition module is used to collect the current at the power supply end of the elevator drive motor, the current excitation signal and the main shaft speed; the signal hardware processing module is used to receive the current, speed and current excitation signals collected by the data acquisition module and filter the current excitation signals; the communication module is used to send the processed current, speed and current excitation signals to the cloud server; the intelligent diagnosis module is used to diagnose the elevator operation status data according to the processed current, speed and current excitation signals and store the elevator operation status data in the memory; the display terminal is used to retrieve the elevator status data in the memory and visualize the elevator status data.

[0007] In a possible design, the signal hardware processing module includes: a filter circuit, an A / D converter, and a CPU controller; wherein,

[0008] The filter circuit is used to amplify the current excitation signal and suppress the interference signal;

[0009] The A / D converter is used to perform A / D conversion on the current excitation signal after amplification and suppression of interference signals;

[0010] The CPU controller is used to package the digital signal converted by the A / D converter and send the packaged information to the communication module.

[0011] In one possible design, the filter circuit includes: a second-order low-pass filter, a second-order high-pass filter, a switched capacitor low-pass filter, and a multi-path negative feedback low-pass filter;

[0012] Among them, the second-order low-pass filter and the second-order high-pass filter form a passband filter circuit to attenuate useless signals in the frequency band other than 50Hz and 3KHz in the current excitation signal;

[0013] A switched capacitor low-pass filter is used to set the cutoff frequency;

[0014] The multi-channel negative feedback low-pass filter is used to select the DC bias current and filter out the clock ripple caused by the switch capacitor low-pass filter to obtain a filtered current excitation signal.

[0015] In a possible design, the second-order low-pass filter includes: an operational amplifier U1, a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a first capacitor C1 and a second capacitor C2.

[0016] In one possible design, the second-order high-pass filter includes an operational amplifier U2, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a tenth resistor R10, a third capacitor C3, and a fourth capacitor C4.

[0017] In one possible design, the switched capacitor low-pass filter includes: a filter U3, an eleventh resistor R11, a twelfth resistor R12, a thirteenth resistor R13, a fourteenth resistor R14, a fifth capacitor C5, a sixth capacitor C6, and a seventh capacitor C7.

[0018] In a possible design, the multi-channel negative feedback low-pass filter includes an operational amplifier U4, a fifteenth resistor R15, a sixteenth resistor R16, a seventeenth resistor R17, an eighth capacitor C8, a ninth capacitor C9, a tenth capacitor C10, and a DC bias V1.

[0019] In a possible design, the chip used for the second-order low-pass filter is AD822, the chip used for the second-order high-pass filter is AD824, the chip used for the switched capacitor low-pass filter is LTC1569, and the chip used for the multi-channel negative feedback low-pass filter is AD822.

[0020] In a possible design, it also includes a power supply circuit connected to the signal hardware processing module and the communication module.

[0021] In a possible design, the intelligent diagnosis module includes: a hoist operation mode judgment unit, a wavelet packet decomposition and reconstruction model unit, a high-order harmonic content calculation model unit, a BP prediction model unit, and a diagnosis result unit for the operation status of different parts of the hoist;

[0022] The hoist operation mode judgment unit is used to analyze and judge the processed current and spindle speed signals, filter out the current excitation signal of the hoist during acceleration and deceleration, and output the current excitation signal of the hoist in a uniform speed operation mode;

[0023] The wavelet packet decomposition and reconstruction model unit is used to perform low-pass and high-pass combination on the processed current excitation signal, and divide the signal into different frequency bands through a conjugate orthogonal filter. The energy value of the wavelet packet coefficient is calculated using the wavelet coefficients of each scale in different frequency bands, and the standard fault feature vector is obtained through the energy value;

[0024] A high-order harmonic content calculation model unit is used to calculate the ratio of a standard fault characteristic vector to a basic sine wave characteristic vector to obtain a high-order harmonic content characteristic vector;

[0025] The BP prediction model unit is used to establish a training set and perform fault diagnosis on the signal to be diagnosed by taking the high-order harmonic content feature vector as input, and output the diagnosis result;

[0026] The diagnostic result unit for the operating status of different parts of the elevator is used to receive the diagnostic results output by the BP prediction model unit and package the diagnostic results and send them to the storage.

[0027] The beneficial effects of the present invention are:

[0028] The present invention provides an intelligent diagnosis system for the operation status of a hoist based on fusion diagnosis technology. Compared with the prior art, it has the following beneficial effects:

[0029] On the basis of harmonic fault diagnosis theory, the data acquisition module is used to collect the current at the power supply end of the hoist drive motor, the current excitation signal and the speed of the main shaft, and the signal hardware processing module performs calculation processing on the current excitation signal; the communication module uploads the processed current excitation signal to the intelligent diagnosis module; the platform layer performs diagnostic analysis on the uploaded data through the intelligent diagnosis module and stores it in the memory; the user display terminal accesses the cloud server and reads the memory data for visual display. Intelligent diagnosis of the hoist operation status is realized. While not affecting normal production, the operation and maintenance personnel do not need to check the hoist equipment by themselves. They only need to view the diagnosis results of the hoist operation status through the user display terminal, which greatly reduces the workload of the operation and maintenance personnel. In terms of economic benefits, the cost of regular shutdown inspection and maintenance is reduced, the production efficiency is improved, and the enterprise's interests are maximized.

[0030] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0032] Figure 1 is a schematic diagram of an intelligent diagnosis system for hoist operation status based on fusion diagnosis technology in an embodiment of the present invention;

[0033] Figure 2 is a functional module schematic diagram of a signal hardware processing module in an embodiment of the present invention;

[0034] Figure 3 is a functional module schematic diagram of a filter circuit in an embodiment of the present invention;

[0035] Figure 4 is a filter circuit diagram in an embodiment of the present invention;

[0036] Figure 5 is a functional module schematic diagram of an intelligent diagnosis module in an embodiment of the present invention;

[0037] Figure 6 It is a schematic diagram of circuit signal measurement in an implementation case of the present invention.

[0038] Icons: 1-data acquisition module; 2-signal hardware processing module; 3-communication module; 4-cloud server; 5-intelligent diagnosis module; 6-memory; 7-user display terminal; 8-power supply circuit; 211-second-order low-pass filter; 212-second-order high-pass filter; 213-switched capacitor low-pass filter; 214-multi-channel negative feedback low-pass filter; 231-A / D converter; 501-elevator operation mode judgment unit; 502-wavelet packet decomposition and reconstruction model unit; 503-high-order harmonic content calculation model unit; 504-BP prediction model unit; 505-elevator operation status diagnosis result unit of different parts. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be noted that although the description of these embodiments is used to help understand the present invention, it does not constitute a limitation of the present invention.

[0040] Please refer to 1 to Figure 6 The embodiment of the present invention proposes an intelligent diagnosis system for the operation status of a hoist based on fusion diagnosis technology, including: a data acquisition module 1, a signal hardware processing module 2, a communication module 3, a cloud server 4, an intelligent diagnosis module 5, a memory 6 and a user display terminal 7, wherein the data acquisition module 1 and the communication module 3 are respectively connected to the signal hardware processing module 2, the cloud server 4 includes a memory 6 and an intelligent diagnosis module 5, and the communication module 3 and the user display terminal 7 are respectively connected to the cloud server 4 in communication; wherein,

[0041] The data acquisition module 1 is used to collect the current, current excitation signal and main shaft speed of the hoist drive motor power supply end; the signal hardware processing module 2 is used to receive the current, speed and current excitation signal collected by the data acquisition module 1 and filter the current excitation signal; the communication module 3 is used to send the processed current, speed and current excitation signal to the cloud server 4, and the intelligent diagnosis module 5 is used to diagnose the motor status data of the hoist according to the processed current, speed and current excitation signal and store the hoist status data in the memory 6; the display terminal is used to retrieve the motor status data in the memory 6 and visualize the motor status data; among them, it can be clearly seen that the motor status data can be real-time motor status data or historical motor status data.

[0042] The intelligent diagnosis system for the operation status of the hoist based on the fusion diagnosis technology includes: a data acquisition module 1, a signal hardware processing module 2, a communication module 3, a cloud server 4, an intelligent diagnosis module 5, a memory 6, and a user display terminal 7, wherein: the data acquisition module 1 installs a current transformer at the power supply end of the hoist drive motor, installs a speed sensor at the transmission shaft at the rear end of the reducer, and installs a current magnetostrictive sensor next to the power supply cable of the drive motor for collecting current, current excitation signal and the rotation speed of the main shaft; the signal hardware processing module 2 is connected to the data acquisition module 1 by wires, and is used to process the collected current, current excitation signal and the rotation speed of the main shaft; the communication module 3 is connected to the signal hardware processing module 2 by wires, and is used to process the processed current, current excitation signal and the rotation speed of the main shaft. The spindle speed is uploaded to the cloud server 4; the cloud server 4 is wirelessly connected to the communication module 3, and is used to receive the current, current excitation signal and spindle speed of the motor to be diagnosed after being processed by the signal hardware processing module 2, and to manage the data transmission between the intelligent diagnosis module 5 and the memory 6; the intelligent diagnosis module 5 is wirelessly connected to the cloud server 4, and is used to process and diagnose the current, current excitation signal and spindle speed of the motor to be diagnosed after being processed by the signal hardware processing module 2, and upload the diagnosis result to the memory 6; the memory 6 is used to store the current, current excitation signal and spindle speed and diagnosis result data of the motor to be diagnosed after being processed by the signal hardware processing module 2; the user display terminal 7 is wirelessly connected to the cloud server 4, and is used to access the cloud server 4 and view the diagnosis result data.

[0043] At present, the detection of electrical equipment is mainly to reduce power outage time and improve the reliability of equipment operation. The maintenance strategy is changing from preventive testing to condition-based maintenance. Therefore, based on the harmonic diagnosis technology and combined with the current Internet of Things technology, a set of intelligent diagnosis system for the health status of the elevator is built to realize the maintenance mode, from the traditional TBM mode to the CDT mode. Through the remote all-weather unmanned monitoring, detection, and diagnosis of the elevator equipment, and finally returning the diagnosis results to the user, the user can more intuitively understand the operating status of the elevator equipment, so as to achieve early repair of minor problems, formulate accurate maintenance plans for deterioration, reduce equipment downtime, improve productivity and reduce accident rates.

[0044] In summary, based on the harmonic fault diagnosis theory, the data acquisition module 1 is used to collect the motor current, current excitation signal and spindle speed, and the signal hardware processing module 2 performs calculations on the current, current excitation signal and spindle speed; the communication module 3 uploads the processed current, current excitation signal and spindle speed to the intelligent diagnosis module 5; the platform layer performs diagnostic analysis on the uploaded data through the intelligent diagnosis module 5 and stores it in the memory 6; the user display terminal 7 accesses the cloud server 4 and reads the data in the memory 6 for visual display. The intelligent diagnosis of the health status of the elevator is realized. While not affecting normal production, the operation and maintenance personnel do not need to check the elevator equipment by themselves. They only need to view the diagnosis results of the elevator operation status through the user display terminal 7, which greatly reduces the workload of the operation and maintenance personnel, reduces the cost of regular shutdown inspection and maintenance in terms of economic benefits, improves production efficiency, and maximizes corporate interests.

[0045] In a possible design, the signal hardware processing module 2 includes: a filtering circuit, an A / D converter 231, and a CPU controller; wherein the filtering circuit is used to amplify the current excitation signal and suppress the interference signal; the A / D converter 231 is used to perform A / D conversion on the current excitation signal after amplification and suppression of the interference signal; the CPU controller is used to package the digital signal converted by the A / D converter 231 and send the packaged information to the communication module 3.

[0046] The communication module 3 may include a 4G module and an antenna, and the 4G module transmits the packaged data to the cloud server 4 through the antenna. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology also includes a power supply circuit 8 connected to the signal hardware processing module 2 and the communication module 3.

[0047] The signal hardware processing module 2 includes a filter circuit, an A / D converter 231, a CPU controller, a 4G module and an antenna, and also includes a power supply circuit 8 connected to the signal hardware processing module 2 and the communication module 3 for power supply. Among them: the filter circuit is connected to the current excitation sensor 1, and is used to amplify the collected current excitation signal and suppress the common mode signal interference signal; the A / D converter 231 is connected to the current transformer, the speed sensor and the filter circuit wire, and is used to perform A / D conversion on the collected current, current excitation signal and the speed of the spindle; the CPU controller is connected to the A / D converter 231 wire, and is used to package the digital signal converted by the A / D converter 231; the 4G module is connected to the CPU controller wire, and the 4G module is used to receive the package information sent by the CPU controller and complete the sending function through the antenna; the antenna is connected to the 4G module wire, and is used to increase the sending distance of the 4G module sending function and increase the signal gain.

[0048] In a possible design, the filtering circuit includes: a second-order low-pass filter 211 , a second-order high-pass filter 212 , a switched capacitor low-pass filter 213 , and a multi-path negative feedback low-pass filter 214 .

[0049] The second-order low-pass filter 211 and the second-order high-pass filter 212 form a passband filter circuit to attenuate useless signals in the current excitation signal except for the frequency band of 50 Hz and 3 kHz; the switched capacitor low-pass filter 213 is used to set the cut-off frequency.

[0050] The multi-channel negative feedback low-pass filter 214 is used to select the DC bias current and filter out the clock ripple caused by the switch capacitor low-pass filter 213 to obtain a filtered current excitation signal.

[0051] The second-order low-pass filter 211 and the second-order high-pass filter 212 form a passband filter circuit, which attenuates useless signals in the mid-frequency band of the signal other than 50Hz and 3KHz and prevents some high-frequency signals from interfering with the subsequent switched capacitor low-pass filter 213, resulting in aliasing signal phenomenon; after receiving the signal, the switched capacitor low-pass filter 213 LTC1569 chip sets the cut-off frequency by designing relevant circuit parameters, and its cut-off frequency calculation formula is:

[0052]

[0053] Where f OUT -Cut-off frequency, R 14 -R x The resistance of the terminating resistor, N-frequency division coefficient. In order to ensure that the signal outside the frequency band is sufficiently attenuated, the filter circuit in the present invention is directly connected to the DC5.0V DC power supply, so N=16. The target cut-off frequency f OUT =2kHz, reverse the cutoff frequency to calculate R 14 =40kΩ. Finally, the multi-channel negative feedback low-pass filter 214 selects a suitable DC bias current V1 to filter out the clock ripple caused by the smoothing switch capacitor low-pass filter 213, and finally obtains an accurate current excitation signal.

[0054] By utilizing fusion diagnosis technology, the current at the power supply end of the hoist drive motor, the current excitation signal and the rotation speed of the main shaft are collected to diagnose the health status of the hoist. Taking into account the interference of noise signals of about 3KHz to 3.6KHz aliased during signal acquisition, a hoist operation mode judgment unit 501 is added to the intelligent diagnosis system of the health status of the hoist based on fusion diagnosis technology to judge the operation mode of the hoist. The current and the rotation speed signal of the main shaft are used to filter out the current excitation signal when the hoist is running at a constant speed. The signal is denoised by the anti-aliasing filter circuit and the wavelet packet decomposition and reconstruction model unit 501. At the same time, the BP algorithm intelligent diagnosis module 5 is added to solve the problem of difficult self-learning of the traditional expert diagnosis module, thereby improving the diagnosis rate and accuracy of the health status diagnosis of the hoist.

[0055] In a possible design, the second-order low-pass filter 211 includes: an operational amplifier U1, a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a first capacitor C1 and a second capacitor C2.

[0056] Pin IN is a signal input terminal connected to the first resistor R1, the other end of the first resistor R1 is connected to the second resistor R2, the other end of the second resistor R2 is connected to the pin V+ of the operational amplifier U1, one end of the third resistor R3 is connected to the pin OUT1 of the operational amplifier U1, and the other end is connected to one end of the fourth resistor R4, the other end of the fourth resistor R4 is connected to the ground, one end of the first capacitor C1 is connected between the first resistor R1 and the second resistor R2, and the other end is connected to the pin OUT1 of the operational amplifier U1, one end of the second capacitor C2 is connected to the pin V+ of the operational amplifier U1, and the other end is connected to the ground, the pin V- of the operational amplifier U1 is connected between the third resistor R3 and the fourth resistor R4, and the pin OUT1 of the operational amplifier U1 is a signal output terminal.

[0057] In one possible design, the second-order high-pass filter 212 includes an operational amplifier U2, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a tenth resistor R10, a third capacitor C3, and a fourth capacitor C4.

[0058] One end of the third capacitor C3 is connected to the output end of the second-order low-pass filter 211, and the other end is connected to one end of the fourth capacitor C4, the other end of the fourth capacitor C4 is connected to the negative input terminal V- of the operational amplifier U2, the fifth resistor R5 is connected between the third capacitor C3 and the fourth capacitor C4, and the other end is connected to the pin OUT2 of the operational amplifier U2, one end of the ninth resistor R9 is connected to the negative input terminal V+ of the operational amplifier U2, and the other end is connected to the eighth resistor R8, the other end of the eighth resistor R8 is connected to the ground, one end of the sixth resistor R6 is connected between the eighth resistor R8 and the ninth resistor R9, and the other end is connected to the DC voltage VCC, one end of the seventh resistor R7 is connected between the eighth resistor R8 and the ninth resistor R9, and the other end is connected to the negative input terminal V- of the operational amplifier U2, and the pin OUT2 of the operational amplifier U2 is the signal output end.

[0059] In a possible design, the switched capacitor low-pass filter 213 includes: a filter U3, an eleventh resistor R11, a twelfth resistor R12, a thirteenth resistor R13, a fourteenth resistor R14, a fifth capacitor C5, a sixth capacitor C6, and a seventh capacitor C7.

[0060] One end of the eleventh resistor R11 is connected to the pin OUT2 of the operational amplifier U2, and the other end is connected to the pin IN+ of the filter U3; one end of the twelfth resistor R12 is connected to the +5V power supply, and the other end is connected to the pin IN- of the filter U3; one end of the thirteenth resistor R13 is connected to the ground, and the other end is connected to the pin IN- of the filter U3; one end of the fourteenth resistor R14 is connected to the pin V+ of the filter U3, and the other end is connected to the pin RX of the filter U3; one end of the fifth capacitor C5 is connected to the ground, and the other end is connected to the pin IN- of the filter U3; one end of the sixth capacitor C6 is connected to the pin CLK of the filter U3, and the other end is connected to the ground;

[0061] The pin GND of the filter U3 is connected to the pin IN- of the filter U3, the pin V- of the filter U3 is connected to the ground, and the pin OUT of the filter U3 is the signal output terminal.

[0062] In a possible design, the multi-channel negative feedback low-pass filter 214 includes an operational amplifier U4, a fifteenth resistor R15, a sixteenth resistor R16, a seventeenth resistor R17, an eighth capacitor C8, a ninth capacitor C9, a tenth capacitor C10, and a DC bias V1.

[0063] One end of the eighth capacitor C8 is connected to the pin OUT of the filter U3, and the other end is connected to the fifteenth resistor R15, the other end of the fifteenth resistor R15 is connected to the sixteenth resistor R16, the other end of the sixteenth resistor R16 is connected to the pin V- of the operational amplifier U4, one end of the seventeenth resistor R17 is connected to the middle of the fifteenth resistor R15 and the sixteenth resistor R16, and the other end is connected to the pin OUT of the operational amplifier U4, one end of the ninth capacitor C9 is connected to the middle of the fifteenth resistor R15 and the sixteenth resistor R16, and the other end is connected to the ground, one end of the tenth capacitor C10 is connected to the pin V- of the operational amplifier U4, and the other end is connected to the pin OUT3 of the operational amplifier U4, and the pin V+ of the operational amplifier U4 is connected to the DC bias V1.

[0064] In a possible design, the chip used by the second-order low-pass filter 211 is AD822, the chip used by the second-order high-pass filter 212 is AD824, the chip used by the switched capacitor low-pass filter 213 is LTC1569, and the chip used by the multi-channel negative feedback low-pass filter 214 is AD822.

[0065] This intelligent diagnosis system for the operation status of the hoist based on fusion diagnosis technology has designed hoist current, current excitation signal and spindle speed acquisition modules at the perception layer, and adopts diversified information collection and processing methods to improve the accuracy of diagnosis.

[0066] At the same time, based on the fusion diagnosis technology, a filter circuit is designed at the perception layer, using the anti-aliasing low-pass filter chip LTC1569, and adding a passband filter circuit composed of a second-order low-pass filter 211 and a second-order high-pass filter 212 in the previous stage to prevent aliasing from occurring in the output when the frequency in the input signal is near the LTC1569 sampling frequency or an integer multiple of the sampling frequency. It is mainly used to remove the aliased noise signal of about 3KHz to 3.6KHz in the current excitation sensor extraction signal. In order to minimize the interference effect, a multi-channel negative feedback low-pass filter 214 is added to the post-stage of the anti-aliasing low-pass filter chip LTC1569. The purpose is to smooth the clock ripple caused by the switched capacitor low-pass filter 214, increase the feasibility of the signal extraction process, and improve the reliability of subsequent signal processing and diagnosis. At the same time, at the perception layer

[0067] At the platform level, the intelligent diagnosis module 5 is used to improve the speed and accuracy of motor health status diagnosis, improve the timeliness and reliability of motor operation in terms of safety, and reduce sudden failures.

[0068] In a possible design, the intelligent diagnosis module 5 includes: 501-elevator operation mode judgment unit, wavelet packet decomposition and reconstruction model unit 502, high-order harmonic content calculation model unit 503, BP prediction model unit 504, and elevator different parts operation status diagnosis result unit 505.

[0069] The hoist operation mode judgment unit 501 is used to analyze and judge the processed current and spindle speed signals, filter out the current excitation signal of the hoist during acceleration and deceleration, and output the current excitation signal of the hoist in the uniform speed operation mode;

[0070] The wavelet packet decomposition and reconstruction model unit 502 is used to perform low-pass and high-pass combination on the processed current excitation signal, and divide the signal into different frequency bands through a conjugate orthogonal filter, and calculate the energy value of the wavelet packet coefficient in different frequency bands using the wavelet coefficients of each scale, and obtain the standard fault feature vector through the energy value;

[0071] The high-order harmonic content calculation model unit 503 is used to calculate the ratio of the standard fault feature vector to the basic sine wave feature vector to obtain the high-order harmonic content feature vector.

[0072] The BP prediction model unit 504 is used to establish a training set and perform fault diagnosis on the signal to be diagnosed by taking the high-order harmonic content feature vector as input, and output the diagnosis result.

[0073] The unit 505 for diagnosing the operating status of different parts of the elevator is used to receive the diagnosis results output by the BP prediction model unit 503 and package the diagnosis results and send them to the memory 6.

[0074] The intelligent diagnosis module 5 includes: a hoist operation mode judgment unit 501, a wavelet packet decomposition and reconstruction model unit 502, a high-order harmonic content calculation model unit 503, a BP prediction model unit 504, and a motor different parts operation status diagnosis result unit 505, which is used to perform core analysis and diagnosis on the processed current excitation signal uploaded by the communication module 3, and upload the diagnosis result to the cloud server 4. The wavelet packet decomposition and reconstruction model unit 502 performs low-pass and high-pass combination on the processed current excitation signal, and divides the signal into different frequency bands through a conjugate orthogonal filter, and uses the wavelet coefficients of each scale in different frequency bands to calculate the energy value E of the wavelet packet coefficient. H , and finally the energy is normalized:

[0075]

[0076] Where: M() represents the mean value of the wavelet packet coefficient energy; D σ Indicates the standard deviation of wavelet coefficient energy. Finally, the standard fault feature vector is obtained:

[0077]

[0078] The high-order harmonic content calculation model unit 503 obtains a high-order harmonic content feature vector by calculating the ratio of the standard fault feature vector obtained by the wavelet packet decomposition and reconstruction model unit 502 to the basic sinusoidal wave feature vector; the BP prediction model unit 504 uses the high-order harmonic content feature vector obtained by the high-order harmonic content calculation model unit 503 as input, establishes a training set and performs fault diagnosis on the signal to be diagnosed, and finally outputs the diagnosis result; the diagnosis results of the operating status of different parts of the elevator are used to receive the diagnosis results output by the BP prediction model, and the diagnosis results are packaged and sent to the cloud server 4.

[0079] See also Figure 6 , is a schematic diagram of an intelligent diagnosis system for the operation status of a hoist based on the fusion diagnosis technology in an embodiment of the present invention. The intelligent diagnosis system for the operation status of a hoist based on the fusion diagnosis technology includes: a hoist drive motor, a power supply, a load, a coupling, a current transformer, a current history sensor, a speed sensor, a signal hardware processing board, a communication board, a cloud server, an intelligent diagnosis platform, a memory, and a user display terminal. The hoist drive motor obtains power through the power supply and drives the load to perform experimental operation. A current transformer is installed at the power supply end of the hoist drive motor, a speed sensor is installed at the transmission shaft at the rear end of the reducer, and a current magnetostrictive sensor is installed next to the power supply cable of the drive motor for collecting current, current excitation signals and the speed of the main shaft. The collected signals are sent to the communication board through the signal hardware processing board and then transmitted to the cloud server. The cloud server sends the data to the intelligent diagnosis platform. After the diagnosis results of the operation status of different parts of the hoist are obtained, they are uploaded back to the cloud server. The cloud server stores the diagnosis results in the memory for users to call and view the data later. The user terminal accesses the cloud server through the network to view the diagnosis results.

[0080] In short, the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent diagnosis system for hoist operation status based on fusion diagnosis technology, characterized in that: include: A data acquisition module, a signal hardware processing module, a communication module, a cloud server, an intelligent diagnosis module, a memory and a user display terminal, wherein the data acquisition module and the communication module are respectively connected to the signal hardware processing module, the cloud server includes a memory and an intelligent diagnosis module, and the communication module and the user display terminal are respectively connected to the cloud server in communication; wherein, The data acquisition module is used to collect the current, current excitation and spindle speed signals of the power supply end of the elevator drive motor; the signal hardware processing module is used to receive the current, speed and current excitation signals collected by the data acquisition module and filter the current excitation signals; the communication module is used to send the processed current, speed and current excitation signals to the cloud server, and the intelligent diagnosis module is used to diagnose the elevator drive motor status data according to the processed current, speed and current excitation signals and store the elevator status data in the memory; the display terminal is used to retrieve the elevator status data in the memory and visualize the elevator status data.

2. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 1 is characterized in that: The signal hardware processing module includes: a filter circuit, an A / D converter and a CPU controller; wherein, The filter circuit is used to amplify the current excitation signal and suppress interference signals; The A / D converter is used to perform A / D conversion on the current excitation, current and speed signals after amplifying and suppressing the interference signal; The CPU controller is used for packaging the digital signal converted by the A / D converter and sending the packaged information to the communication module.

3. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 2 is characterized in that: The filtering circuit includes: a second-order low-pass filter, a second-order high-pass filter, a switched capacitor low-pass filter and a multi-channel negative feedback low-pass filter; Wherein, the second-order low-pass filter and the second-order high-pass filter form a passband filter circuit to attenuate useless signals in the frequency band other than 50 Hz and 3 KHz in the current excitation signal; The switched capacitor low-pass filter is used to set the cut-off frequency; The multi-channel negative feedback low-pass filter is used to select a DC bias current and filter out the clock ripple caused by the switch capacitor low-pass filter to obtain a filtered current excitation signal.

4. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 3 is characterized in that: The second-order low-pass filter includes: an operational amplifier U1, a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a first capacitor C1 and a second capacitor C2.

5. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 3 is characterized in that: The second-order high-pass filter includes an operational amplifier U2, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a tenth resistor R10, a third capacitor C3, and a fourth capacitor C4.

6. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 3 is characterized in that: The switched capacitor low-pass filter includes: a filter U3, an eleventh resistor R11, a twelfth resistor R12, a thirteenth resistor R13, a fourteenth resistor R14, a fifth capacitor C5, a sixth capacitor C6, and a seventh capacitor C7.

7. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 3 is characterized in that: The multi-channel negative feedback low-pass filter includes an operational amplifier U4, a fifteenth resistor R15, a sixteenth resistor R16, a seventeenth resistor R17, an eighth capacitor C8, a ninth capacitor C9, a tenth capacitor C10, and a DC bias V1.

8. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 3 is characterized in that: The chip used by the second-order low-pass filter is AD822, the chip used by the second-order high-pass filter is AD824, the chip used by the switched capacitor low-pass filter is LTC1569, and the chip used by the multi-channel negative feedback low-pass filter is AD822.

9. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 1 is characterized in that: It also includes a power supply circuit module connected to the signal hardware processing module and the communication module.

10. The intelligent diagnosis system for hoist operation status based on fusion diagnosis technology according to claim 1 is characterized in that: The intelligent diagnosis module includes: a hoist operation mode judgment unit, a wavelet packet decomposition and reconstruction model unit, a high-order harmonic content calculation model unit, a BP prediction model unit, and a hoist operation status diagnosis result unit of different parts; wherein, The hoist operation mode judgment unit is used to analyze and judge the processed current and spindle speed signals, filter out the current excitation signal of the hoist during acceleration and deceleration, and output the current excitation signal of the hoist in a uniform speed operation mode; The wavelet packet decomposition and reconstruction model unit is used to perform low-pass and high-pass combination on the processed current excitation signal, divide the signal into different frequency bands through a conjugate orthogonal filter, calculate the energy value of the wavelet packet coefficient in different frequency bands using the wavelet coefficients of each scale, and obtain the standard fault feature vector through the energy value; The high-order harmonic content calculation model unit is used to calculate the ratio of the standard fault feature vector to the basic sine wave feature vector to obtain the high-order harmonic content feature vector; The BP prediction model unit is used to establish a training set and perform fault diagnosis on the signal to be diagnosed by taking the high-order harmonic content feature vector as input, and output a diagnosis result; The operating status diagnosis result unit of different parts of the elevator is used to receive the diagnosis results output by the BP prediction model unit, and package the diagnosis results and send them to the memory.

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