Elevator vibration monitoring and fault diagnosis method, device and equipment
By preprocessing and determining the status of elevator data on a local processor, and combining this with a status-based data upload scheme, the problems of resource waste and low diagnostic efficiency in elevator vibration monitoring and fault diagnosis are solved, achieving efficient fault diagnosis.
Patent Information
- Application Number
- CN202511986604.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-02-27
AI Technical Summary
Existing elevator vibration monitoring and fault diagnosis methods consume massive amounts of cloud processor resources, leading to resource waste, and fault diagnosis lacks specificity and efficiency.
By preprocessing the elevator data collected by the vibration sensor on the local processor, the elevator's operating status is determined, and a data upload scheme is determined based on the status, thereby realizing status-based control of elevator data, and then fault diagnosis is performed in the cloud.
It improves the targeting and efficiency of cloud-based fault diagnosis, reduces resource waste, and enhances the rationality of data upload and the accuracy of fault diagnosis.
Smart Images
Figure CN121573531A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of elevator fault diagnosis, in particular to an elevator vibration monitoring and fault diagnosis method, device, equipment and readable storage medium. BACKGROUND
[0002] With the continuous growth of urban building height and elevator ownership, as a high-frequency operating mechanical and electrical equipment, the operation safety and reliability of the elevator have attracted widespread attention. In the long-term operation process of the elevator, key mechanical components such as the traction system and the guide system are prone to abnormal vibration due to factors such as wear, looseness or assembly deviation. If it cannot be found and handled in time, it may lead to fault expansion or even cause safety accidents. Therefore, the elevator fault diagnosis and early warning technology based on the running state data has gradually become an important direction of research and engineering application.
[0003] At present, in the aspect of fault diagnosis method, the research focus is concentrated on intelligent diagnosis technology based on vibration signal analysis. The existing method usually deploys an acceleration sensor on the elevator car or the key position to collect the vibration signal in the operation process of the elevator, and analyzes and processes through the cloud or the background server. However, the data generated during the operation of the elevator is massive, and the elevator is in normal state most of the time. A large amount of data will occupy too many computing resources of the cloud processor, causing resource waste.
[0004] Therefore, there is an urgent need for an elevator vibration monitoring and fault diagnosis method that can overcome the above-mentioned defects. SUMMARY
[0005] The purpose of the present application is to provide an elevator vibration monitoring and fault diagnosis method, device, equipment and readable storage medium. The elevator vibration monitoring and fault diagnosis method of the present application pre-processes the elevator data collected by the vibration sensor in the local processor and determines the running state of the elevator, so that the state can be distinguished before the elevator data is uploaded to the cloud, and the corresponding data uploading scheme is determined according to different elevator states, so as to realize the state control of the elevator data uploading behavior. On this basis, the cloud processor performs fault diagnosis on the elevator data screened by the state, so that the fault diagnosis process is established on the basis of the clear elevator running state, which is beneficial to improve the pertinence and efficiency of the cloud fault diagnosis processing.
[0006] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: In a first aspect, the present application provides an elevator vibration monitoring and fault diagnosis method, which comprises: obtaining elevator data of a target elevator based on a vibration sensor, and pre-processing the elevator data based on a local processor to obtain an elevator state of the target elevator; the elevator state includes a static state, a normal transportation state and an abnormal vibration state; According to the elevator state of the target elevator, an uploading scheme of the elevator data is determined, and the elevator data is uploaded to a cloud processor based on the uploading scheme; Based on the cloud processor, fault diagnosis is performed on the elevator data to obtain a fault diagnosis result.
[0007] In some embodiments, according to the elevator state of the target elevator, an uploading scheme of the elevator data is determined, and the elevator data is uploaded to a cloud processor based on the uploading scheme, including: If the target elevator is in a static state, the car attitude angle data in the elevator data is uploaded to the cloud processor based on a preset frequency; If the target elevator is in a normal transportation state, representative data in the elevator data is uploaded to the cloud processor based on a preset frequency; If the target elevator is in an abnormal jitter state, the elevator data is uploaded to the cloud processor in real time.
[0008] In some embodiments, based on the cloud processor, fault diagnosis is performed on the elevator data to obtain a fault diagnosis result, including: Time domain analysis and frequency domain transformation are performed on the elevator data to obtain a vibration signal spectrum; According to the mechanical parameters of the target elevator, the theoretical characteristic frequency of each rotating component in the target elevator is calculated; Based on the theoretical characteristic frequency and the vibration signal spectrum, fault diagnosis is performed on the elevator data to obtain a fault diagnosis result.
[0009] In some embodiments, based on the theoretical characteristic frequency and the vibration signal spectrum, fault diagnosis is performed on the elevator data to obtain a fault diagnosis result, including: An effective segment related to each rotating component in the target elevator is extracted from the vibration signal spectrum; Based on the theoretical characteristic frequency and the effective segment, fault diagnosis is performed on the elevator data to obtain a fault diagnosis result.
[0010] In some embodiments, based on the theoretical characteristic frequency and the effective segment, fault diagnosis is performed on the elevator data to obtain a fault diagnosis result, including: A dynamic threshold model of the theoretical vibration energy amplitude corresponding to the theoretical characteristic frequency is established; The real-time vibration energy amplitude is extracted from the effective segment; The real-time vibration energy amplitude is compared with the dynamic threshold model to obtain a fault diagnosis result.
[0011] In some embodiments, based on the local processor, the elevator data is preprocessed to obtain the elevator state of the target elevator, including: The elevator data is filtered and denoised based on the local processor; The elevator data is input into a pre-trained state recognition model, and an elevator state of the target elevator is output.
[0012] In a second aspect, the present application further provides an elevator vibration monitoring and fault diagnosis device, which comprises: a state determination module, configured to acquire elevator data of the target elevator based on the vibration sensor, and to preprocess the elevator data based on the local processor to obtain an elevator state of the target elevator; the elevator state comprises a static state, a normal transportation state and an abnormal jitter state; a data uploading module, configured to determine an uploading scheme of the elevator data according to the elevator state of the target elevator, and to upload the elevator data to the cloud processor based on the uploading scheme; a fault diagnosis module, configured to perform fault diagnosis on the elevator data based on the cloud processor to obtain a fault diagnosis result.
[0013] In a third aspect, the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the elevator vibration monitoring and fault diagnosis method provided in the first aspect when executing the computer program.
[0014] In a fourth aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executable on the processor to implement the elevator vibration monitoring and fault diagnosis method provided in the first aspect.
[0015] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, wherein the computer program is executable on the processor to implement the elevator vibration monitoring and fault diagnosis method provided in the first aspect.
[0016] The elevator vibration monitoring and fault diagnosis method in the present application first acquires elevator data of a target elevator based on a vibration sensor, and preprocesses the elevator data based on a local processor to obtain an elevator state of the target elevator; then determines an uploading scheme of the elevator data according to the elevator state of the target elevator, and uploads the elevator data to a cloud processor based on the uploading scheme; finally, performs fault diagnosis on the elevator data based on the cloud processor to obtain a fault diagnosis result. By preprocessing the elevator data collected by the vibration sensor in the local processor and determining the elevator running state, the state of the elevator data can be distinguished before being uploaded to the cloud, and the corresponding data uploading scheme is determined according to the different elevator states, so as to realize the state control of the elevator data uploading behavior; on this basis, the cloud processor performs fault diagnosis on the elevator data screened by the state, so that the fault diagnosis process is established on the basis of the clear elevator running state, which is beneficial to improving the pertinence and efficiency of the cloud fault diagnosis processing.
[0017] The above description is only a summary of the technical solutions of the present application. In order to make the technical means of the present application clearer and can be implemented according to the content of the description, the following will be described in detail with the preferred embodiments of the present application and with the accompanying drawings as follows. BRIEF DESCRIPTION OF DRAWINGS
[0018] Figure 1 A flowchart of an elevator vibration monitoring and fault diagnosis method according to an embodiment of the present application is shown in the figure. Figure 2 A structure diagram of a target elevator according to an embodiment of the present application is shown in the figure. Figure 3 A structure diagram of a target elevator car roof and car bottom according to an embodiment of the present application is shown in the figure. Figure 4 A structure diagram of a target elevator control cabinet according to an embodiment of the present application is shown in the figure. Figure 5 A data flow diagram according to an embodiment of the present application is shown in the figure. Figure 6 A flowchart of another elevator vibration monitoring and fault diagnosis method according to an embodiment of the present application is shown in the figure. Figure 7 An elevator vibration monitoring and fault diagnosis device according to an embodiment of the present application is shown in the figure. Figure 8 Another elevator vibration monitoring and fault diagnosis device according to an embodiment of the present application is shown in the figure. Figure 9 An electronic device structure diagram according to an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION
[0019] The technical solutions of the present application will be described in detail below with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application. It should be noted that the description of "one embodiment", "embodiment", "example embodiment" and the like in the specification means that the described embodiment can include specific features, structures or characteristics, but not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not mean the same embodiment. Furthermore, when a specific feature, structure or characteristic is described in combination with an embodiment, it is indicated that such feature, structure or characteristic is combined with other embodiments within the knowledge of those skilled in the art, whether or not it is explicitly described.
[0020] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as there is no conflict.
[0021] In some embodiments, as shown in Figure 1 A flowchart of an elevator vibration monitoring and fault diagnosis method is provided, and the specific method comprises: S101, obtaining elevator data of a target elevator based on a vibration sensor, and pre-processing the elevator data based on a local processor to obtain an elevator state of the target elevator.
[0022] The elevator state includes a stationary state, a normal transportation state, and an abnormal jitter state.
[0023] Specifically, the vibration sensor is fixedly installed at the center of the bottom of the elevator car, and has a built-in three-axis acceleration sensor chip for collecting original vibration data of the car at a frequency of not less than 1000 Hz; the unit is integrated with an edge computing module for real-time processing of the original vibration data to identify the three running states of the elevator, i.e., the stationary state, the normal transportation state, and the abnormal jitter state, and to determine the data upload strategy according to different states.
[0024] Optionally, the process of pre-processing the elevator data based on the local processor to obtain the elevator state of the target elevator comprises: filtering and noise reduction processing of the elevator data based on the local processor; inputting the elevator data into a pre-trained state recognition model to output the elevator state of the target elevator.
[0025] Specifically, the local processor can first perform filtering and noise reduction processing on the elevator data to filter out noise in the elevator data and increase the accuracy of the elevator data, and then input the elevator data into the pre-trained state recognition model to preliminarily judge the elevator state of the target elevator.
[0026] S102, determining an upload scheme of the elevator data according to the elevator state of the target elevator, and uploading the elevator data to a cloud processor based on the upload scheme.
[0027] Specifically, since the elevator is not in an abnormal state most of the time, it is not necessary to upload the elevator data to the cloud processor all the time, which will cause the resources of the cloud processor to be wasted. In order to avoid this situation, different upload schemes can be selected according to the elevator state.
[0028] Optionally, if the target elevator is in a stationary state, the car attitude angle data in the elevator data is uploaded to the cloud processor based on a preset frequency; if the target elevator is in a normal transportation state, representative data in the elevator data is uploaded to the cloud processor based on a preset frequency; and if the target elevator is in an abnormal jitter state, the elevator data is uploaded to the cloud processor in real time.
[0029] For example, if the target elevator is in a stationary state, the car attitude angle data is calculated and uploaded once per minute; if the target elevator is in a normal transportation state, the elevator data is compressed by sliding window averaging and only about 1% of the representative data is uploaded; if the target elevator is in an abnormal jitter state, the full amount of high-frequency data in this period is uploaded without compression. If the upload fails, the elevator data is stored in the local storage system for re-upload.
[0030] It should be noted that the transmission of elevator data is completed by the power line carrier transmission unit and the network aggregation unit. The power line carrier transmission unit is composed of a pair of power carrier network transmitters (power cats) installed on the outside of the elevator control cabinet and the top of the car. This unit uses the elevator traveling cable to build a stable wired data return channel from the car to the control cabinet, avoiding the instability of wireless transmission. The network aggregation unit is installed in the elevator control cabinet and includes a 4G / 5G router and a switching power supply. The 4G / 5G router is connected to the power line carrier transmission unit through a network cable, receives the processed data from the vibration sensing unit, and packages the data for upload to the cloud processor through the mobile network. The switching power supply provides stable working voltage for the on-site devices.
[0031] S103, fault diagnosis of the elevator data based on the cloud processor, to obtain a fault diagnosis result.
[0032] Specifically, after the elevator data is transmitted to the cloud processor, the cloud processor compares the elevator data with the data of the target elevator in a normal state at a historical time. If they are consistent, it is determined that the target elevator has not failed, otherwise, it is determined that the target elevator has failed.
[0033] Optionally, the elevator data can also be subjected to time domain analysis and frequency domain transformation to obtain a vibration signal spectrum; based on the mechanical parameters of the target elevator, the theoretical characteristic frequencies of each rotating part in the target elevator are calculated; based on the theoretical characteristic frequencies and the vibration signal spectrum, the elevator data is subjected to fault diagnosis to obtain a fault diagnosis result.
[0034] Among them, based on the theoretical characteristic frequencies and the vibration signal spectrum, the elevator data is subjected to fault diagnosis to obtain a fault diagnosis result, including: extracting an effective segment in the vibration signal spectrum related to each rotating part in the target elevator; based on the theoretical characteristic frequencies and the effective segment, the elevator data is subjected to fault diagnosis to obtain a fault diagnosis result.
[0035] Based on the theoretical characteristic frequencies and the effective segment, the elevator data is subjected to fault diagnosis to obtain a fault diagnosis result, including: establishing a dynamic threshold model of the theoretical vibration energy amplitude corresponding to the theoretical characteristic frequencies; extracting a real-time vibration energy amplitude from the effective segment; comparing the real-time vibration energy amplitude with the dynamic threshold model to obtain a fault diagnosis result.
[0036] For example, time-domain analysis and frequency-domain transformation are performed on the continuous vibration signal in the elevator data to extract characteristic indicators reflecting vibration energy, impact and periodicity; then, based on the specific mechanical parameters of the elevator, the theoretical characteristic frequency of each rotating component is calculated, and the energy amplitude of the corresponding frequency point is matched in the spectrum; then, a dynamic threshold model of each characteristic frequency amplitude is established, and by monitoring whether it exceeds the limit and the long-term trend of change, it is determined whether there is a mechanical fault in the traction system and the guiding system, and an early warning information is generated.
[0037] It should be noted that the cloud processor can be an intelligent analysis engine that performs time-domain, frequency-domain, and envelope analysis on the received vibration data, extracting time-domain indicators including root mean square value, kurtosis, and peak factor, as well as calculating spectral energy that matches the theoretical characteristic frequencies of mechanical components such as elevator traction sheaves, anti-cord sheaves, and guide rail nylon wheels. By monitoring the long-term trend and exceedances of characteristic frequency amplitudes, potential faulty components can be located. The cloud processor can also perform attitude monitoring: using acceleration data uploaded when the elevator is stationary, it calculates and monitors the car's pitch and roll angles, analyzes their long-term trends, and provides early warnings of tilt deviations caused by foundation settlement or guide rail wear.
[0038] The elevator vibration monitoring and fault diagnosis method in the above embodiments first acquires elevator data of the target elevator based on vibration sensors, and then preprocesses the elevator data using a local processor to obtain the elevator status of the target elevator. Next, based on the elevator status, a data upload scheme is determined, and the elevator data is uploaded to the cloud processor according to the upload scheme. Finally, the cloud processor performs fault diagnosis on the elevator data to obtain the fault diagnosis result. By preprocessing the elevator data collected by vibration sensors and determining the elevator operating status in the local processor, the elevator data can be classified into different statuses before being uploaded to the cloud, and a corresponding data upload scheme can be determined based on different elevator statuses, thereby achieving state-based control of elevator data upload behavior. Based on this, the cloud processor performs fault diagnosis on the state-filtered elevator data, ensuring that the fault diagnosis process is based on a clear elevator operating status, which helps improve the targeting and efficiency of cloud-based fault diagnosis processing.
[0039] In another embodiment, such as Figure 2 , Figure 3 and Figure 4 As shown, Figure 2 A structural schematic diagram of the target elevator is provided. Figure 3 A structural schematic diagram of the target elevator car top and bottom is provided. Figure 4The structure diagram of a target elevator control cabinet is shown. A fixed switching power supply is provided in the elevator control cabinet to provide stable 12V voltage. A 4G router is installed on the outside of the cabinet, connected with all antennas and SIM cards, and connected with a power carrier network transmitter (Power Cat A) through a network cable. On the car top, a second power carrier transmitter (Power Cat B) and a power extension board are placed. The connection between Power Cat A and B is realized through any two power lines in the traveling cable. On the car bottom, the vibration gateway is firmly pasted on the geometric center of the bottom plate using strong glue, and is connected to the Power Cat B on the car top and the extension board through a 5-meter long comprehensive cable (containing a network cable and a power cable). Thus, a complete data flow channel is formed from the car bottom sensor → car top Power Cat B → traveling cable power line → control cabinet Power Cat A → 4G router → cloud.
[0040] In another embodiment, as shown in Figure 5 Figure 5 The data flow diagram is shown. On the elevator side: power on and initialization of the I2C interface, connection of the acceleration sensor (LIS3DHTR). Run the C language script to continuously read the three-axis acceleration raw data at a sampling rate of about 1700Hz, with a microsecond-level timestamp, and store it as a local log file (such as 2025_10_27_14_30.log) every minute. The edge computing thread is started to read the latest real-time data stream. The original vibration data is filtered by removing outliers, smoothing and Savitzky-Golay filtering, and the elevator state is confirmed according to the filtered results. Compared with the preset threshold value: if the energy is continuously below the threshold value T1, it is determined as “static state”; if the energy is regularly fluctuated between the threshold value T1 and a higher threshold value T2, it is determined as “normal transportation state”; if the energy is instantaneously or continuously above the threshold value T2, it is determined as “abnormal vibration state”. In the static state, the average acceleration value of the past 60 seconds data is calculated every minute, and the attitude angle is estimated and the total inclination angle is calculated and the data is packaged, and the total inclination angle In the normal transportation state, the average value of every 50 original data points (about 30 ms window) is calculated to generate a compressed data point, and the data is packaged. In the abnormal jitter state, all original data points in the triggered period are directly packaged. The MQTT client thread attempts to publish the packaged data to the cloud processor. If successful, the next data packet is processed; if failed (such as network interruption), the data packet is cached as a file to the local disk. The data packet that fails to be published is cached as a file to the local disk. After the network is restored, the MQTT client checks the cache directory and re-sends all unsuccessful data packets in chronological order to ensure that the data is not lost. On the cloud side: the cloud MQTT broker server receives the data packet from each elevator, parses it and stores it in the time series database. The intelligent analysis engine pulls the latest vibration data of the specified elevator at a fixed time (such as every 10 minutes). The FFT transform is performed to obtain the frequency spectrum. The original parameters of the elevator components are obtained, such as the mechanical parameters of the traction sheave diameter D, the traction machine speed N, the counter pulley diameter d, etc. The characteristic frequencies of each component are calculated, such as the traction sheave rotation frequency f_ traction sheave = N / 60, the counter pulley through frequency f_ counter pulley = The traction sheave. In the frequency spectrum, find the amplitude corresponding to these characteristic frequencies f_ traction sheave, f_ counter pulley. Compare the amplitude with the dynamic threshold learned based on historical normal operation data, and observe its trend curve in the past week. If it is found that the amplitude of a certain characteristic frequency is continuously over-standard and shows an upward trend, an early warning is generated.
[0041] In order to more comprehensively show the present scheme, the present embodiment gives an optional way of an elevator vibration monitoring and fault diagnosis method, as shown in Figure 6 S201, obtaining elevator data of a target elevator based on a vibration sensor.
[0042] S202, performing filtering processing and noise reduction processing on the elevator data based on a local processor.
[0043] S203, inputting the elevator data into a pre-trained state recognition model to output an elevator state of the target elevator.
[0044] The elevator state includes a static state, a normal transportation state and an abnormal jitter state. S204, determining an uploading scheme of the elevator data according to the elevator state of the target elevator, and uploading the elevator data to a cloud processor based on the uploading scheme.
[0045] Specifically, if the target elevator is in the static state, the car attitude angle data in the elevator data is uploaded to the cloud processor based on a preset frequency; if the target elevator is in the normal transportation state, representative data in the elevator data is uploaded to the cloud processor based on a preset frequency; if the target elevator is in the abnormal jitter state, the elevator data is uploaded to the cloud processor in real time.
[0046] S205 performs time-domain analysis and frequency-domain transformation on the elevator data to obtain the vibration signal spectrum.
[0047] S206, Calculate the theoretical characteristic frequencies of each rotating component in the target elevator based on the mechanical parameters of the target elevator.
[0048] S207, extract the effective segments from the vibration signal spectrum that are related to each rotating component in the target elevator.
[0049] S208, Establish a dynamic threshold model for the theoretical vibration energy amplitude corresponding to the theoretical characteristic frequency.
[0050] S209 extracts the real-time vibration energy amplitude from the effective segment.
[0051] S210 compares the real-time vibration energy amplitude with the dynamic threshold model to obtain the fault diagnosis result.
[0052] The specific processes of S201-S210 described above can be found in the description of the above method embodiments. Their implementation principles and technical effects are similar, and will not be repeated here.
[0053] Based on the same inventive concept, this application also provides an elevator vibration monitoring and fault diagnosis device for implementing the elevator vibration monitoring and fault diagnosis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more elevator vibration monitoring and fault diagnosis device embodiments provided below can be found in the limitations of the elevator vibration monitoring and fault diagnosis method described above, and will not be repeated here.
[0054] In one embodiment, such as Figure 7 As shown, an elevator vibration monitoring and fault diagnosis device is provided, the device comprising: The state determination module 30 is used to acquire elevator data of the target elevator based on vibration sensors and preprocess the elevator data based on the local processor to obtain the elevator state of the target elevator; the elevator state includes stationary state, normal transportation state and abnormal shaking state. The data upload module 31 is used to determine the elevator data upload scheme based on the elevator status of the target elevator, and upload the elevator data to the cloud processor based on the upload scheme. The fault diagnosis module 32 is used to perform fault diagnosis on elevator data based on the cloud processor and obtain fault diagnosis results.
[0055] In another embodiment, the above Figure 7The data upload module 31 is specifically used for: if the target elevator is stationary, uploading the car attitude angle data in the elevator data to the cloud processor based on a preset frequency; if the target elevator is in normal operation, uploading representative data in the elevator data to the cloud processor based on a preset frequency; if the target elevator is in an abnormal shaking state, uploading the elevator data to the cloud processor in real time.
[0056] In another embodiment, such as Figure 8 As shown above, Figure 7 The fault diagnosis module 32 includes: The spectrum acquisition unit 320 is used to perform time-domain analysis and frequency-domain transformation on elevator data to obtain the vibration signal spectrum. The frequency calculation unit 321 is used to calculate the theoretical characteristic frequencies of each rotating component in the target elevator based on the mechanical parameters of the target elevator. The fault diagnosis unit 322 is used to perform fault diagnosis on elevator data based on theoretical characteristic frequencies and vibration signal spectra, and obtain fault diagnosis results.
[0057] In another embodiment, the above Figure 8 The fault diagnosis unit 322 is specifically used to: extract effective segments related to each rotating component in the target elevator from the vibration signal spectrum; perform fault diagnosis on the elevator data based on the theoretical characteristic frequency and the effective segments, and obtain the fault diagnosis result.
[0058] The fault diagnosis of elevator data is based on theoretical characteristic frequencies and effective segments, and the fault diagnosis results are obtained. This includes: establishing a dynamic threshold model of the theoretical vibration energy amplitude corresponding to the theoretical characteristic frequency; extracting the real-time vibration energy amplitude from the effective segments; and comparing the real-time vibration energy amplitude with the dynamic threshold model to obtain the fault diagnosis results.
[0059] In another embodiment, the above Figure 7 The state determination module 30 is specifically used for: filtering and noise reduction of elevator data based on the local processor; inputting elevator data into the pre-trained state recognition model and outputting the elevator state of the target elevator.
[0060] This application also provides an electronic device, in some embodiments, referring to... Figure 9 As shown, the electronic device 700 includes an input unit 710, a memory 720, a processor 730, and an output unit 740. The memory 720 stores program instructions that can be executed on the processor 730. The processor 730 can execute the elevator vibration monitoring and fault diagnosis method and / or technical solution based on the foregoing embodiments by calling the program instructions. The electronic device 700 can be a mobile terminal device such as a mobile phone or computer.
[0061] In addition, the embodiment of the present application further provides a computer readable storage medium for storing a computer program for executing the elevator vibration monitoring and fault diagnosis method. For example, the computer program instructions, when executed by a computer, can call or provide the method and / or technical solutions according to the present application through the operation of the computer. The program instructions for calling the method of the present application can be stored in a fixed or removable storage medium, and / or transmitted and / or stored in a storage medium running according to the program instructions through a data stream in a broadcast or other signal bearing medium.
[0062] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and optionally, they can be realized by program codes executable by computing devices, so that they can be stored in storage devices for execution by computing devices, or they can be respectively manufactured into integrated circuit modules, or a plurality of modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the present application is not limited to any specific combination of hardware and software.
[0063] The technical features of the above embodiments can be integrated in any manner. In order to make the description simple, all possible integrations of the technical features in the above embodiments are not described, however, as long as the integration of the technical features does not exist contradictions, it should be considered as the scope of the present application.
[0064] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.
Claims
1. A method for monitoring elevator vibration and diagnosing faults, characterized in that, The method includes: Elevator data of the target elevator is acquired based on vibration sensors, and the elevator data is preprocessed based on a local processor to obtain the elevator status of the target elevator; the elevator status includes a stationary state, a normal transportation state, and an abnormal shaking state. Based on the elevator status of the target elevator, determine the elevator data upload scheme, and upload the elevator data to the cloud processor based on the upload scheme; The cloud processor performs fault diagnosis on the elevator data to obtain fault diagnosis results.
2. The elevator vibration monitoring and fault diagnosis method as described in claim 1, characterized in that, Based on the elevator status of the target elevator, a data upload scheme for the elevator is determined, and the elevator data is uploaded to the cloud processor based on the upload scheme, including: If the target elevator is stationary, the car attitude angle data in the elevator data will be uploaded to the cloud processor based on a preset frequency. If the target elevator is in normal operation, representative data from the elevator data will be uploaded to the cloud processor based on a preset frequency. If the target elevator is in an abnormal shaking state, the elevator data will be uploaded to the cloud processor in real time.
3. The elevator vibration monitoring and fault diagnosis method as described in claim 2, characterized in that, Based on the cloud processor, fault diagnosis is performed on the elevator data to obtain fault diagnosis results, including: The elevator data is subjected to time-domain analysis and frequency-domain transformation to obtain the vibration signal spectrum; Based on the mechanical parameters of the target elevator, calculate the theoretical characteristic frequencies of each rotating component in the target elevator; Based on the theoretical characteristic frequency and the vibration signal spectrum, the elevator data is used to perform fault diagnosis, and the fault diagnosis results are obtained.
4. The elevator vibration monitoring and fault diagnosis method as described in claim 3, characterized in that, Based on the theoretical characteristic frequency and the vibration signal spectrum, fault diagnosis is performed on the elevator data to obtain fault diagnosis results, including: Extract the effective segments from the vibration signal spectrum that are related to each rotating component in the target elevator; Based on the theoretical characteristic frequency and the effective segment, the elevator data is used to perform fault diagnosis to obtain the fault diagnosis result.
5. The elevator vibration monitoring and fault diagnosis method as described in claim 4, characterized in that, Based on the theoretical characteristic frequency and the effective segment, fault diagnosis is performed on the elevator data to obtain fault diagnosis results, including: Establish a dynamic threshold model for the theoretical vibration energy amplitude corresponding to the theoretical characteristic frequency; Extract the real-time vibration energy amplitude from the effective segment; The real-time vibration energy amplitude is compared with the dynamic threshold model to obtain the fault diagnosis result.
6. The elevator vibration monitoring and fault diagnosis method as described in claim 1, characterized in that, The elevator data is preprocessed using a local processor to obtain the elevator status of the target elevator, including: The elevator data is filtered and noise-reduced using the local processor. The elevator data is input into a pre-trained state recognition model, which outputs the elevator state of the target elevator.
7. An elevator vibration monitoring and fault diagnosis device, characterized in that, The device includes: The state determination module is used to acquire elevator data of the target elevator based on vibration sensors, and to preprocess the elevator data based on a local processor to obtain the elevator state of the target elevator; the elevator state includes a stationary state, a normal transportation state, and an abnormal shaking state. The data upload module is used to determine the elevator data upload scheme based on the elevator status of the target elevator, and upload the elevator data to the cloud processor based on the upload scheme; The fault diagnosis module is used to perform fault diagnosis on the elevator data based on the cloud processor and obtain fault diagnosis results.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the elevator vibration monitoring and fault diagnosis method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the elevator vibration monitoring and fault diagnosis method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the elevator vibration monitoring and fault diagnosis method according to any one of claims 1 to 6.