Fault detection method and device, storage medium and electronic equipment
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TCL AIR CONDITIONER ZHONGSHAN CO LTD
- Filing Date
- 2026-05-12
- Publication Date
- 2026-07-21
Smart Images
Figure CN122430664A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of detection technology, specifically to a fault detection method, apparatus, storage medium, and electronic device. Background Technology
[0002] In motor drive systems, inverter switching transistors (such as IGBTs (Insulated Gate Bipolar Transistors) and MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors) are key components for achieving motor speed regulation and control. Failure of the switching transistors can have adverse effects on the motor, drive system, and load equipment.
[0003] In response, current fault detection methods typically rely on hardware detection based on overcurrent protection, threshold judgment based on stator current or voltage waveforms, and feature diagnosis based on stator current spectrum analysis to detect faults in switching transistors and take countermeasures. However, these current fault detection methods usually make simple fault judgments based on a single time-domain or frequency-domain indicator, resulting in poor fault detection accuracy and hindering the accurate implementation of countermeasures. Summary of the Invention
[0004] This application provides a fault detection scheme that can effectively improve the accuracy of fault detection for inverter switching transistors.
[0005] The embodiments of this application provide the following technical solutions: According to one embodiment of this application, a fault detection method includes: performing multi-level discrete wavelet transform processing on the stator current signal of a motor to obtain wavelet coefficients of the stator current signal; determining a preset coefficient range in which the wavelet coefficients fall; and determining the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range.
[0006] In some embodiments of this application, the step of performing multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal includes: performing multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain multi-level detail coefficients; and selecting the detail coefficients located in the target layer among the multi-level detail coefficients as the wavelet coefficients.
[0007] In some embodiments of this application, a multi-layer cascaded filter is deployed in the drive controller; the multi-layer discrete wavelet transform processing of the stator current signal of the motor to obtain multi-layer detail coefficients includes: acquiring the stator current signal through the drive controller; and inputting the stator current signal into the multi-layer cascaded filter to sequentially perform discrete wavelet transform processing to obtain the detail coefficients output by the multi-layer filter.
[0008] In some embodiments of this application, before the stator current signal is input into the multi-layer cascaded filter for discrete wavelet transform processing, the method further includes: determining filter parameters according to the type of the motor and / or the inverter switching transistor; and setting parameters of the multi-layer cascaded filter according to the filter parameters.
[0009] In some embodiments of this application, determining the filter parameters based on the type of the motor and / or the inverter switching transistor includes: querying a preset parameter corresponding to the type of the motor and / or the inverter switching transistor from a preset parameter adjustment table; and determining the preset parameter as the filter parameter.
[0010] In some embodiments of this application, determining the filter parameters based on the type of the motor and / or the inverter switching transistor includes: acquiring historical fault diagnosis data corresponding to the type of the motor and / or the inverter switching transistor; and analyzing the historical fault diagnosis data to obtain the filter parameters.
[0011] In some embodiments of this application, determining the fault type of the inverter switch corresponding to the motor based on the preset coefficient range includes: if the preset coefficient range is a first coefficient range, then the fault type of the inverter switch is a disconnection fault; if the preset coefficient range is a second coefficient range, then the fault type of the inverter switch is an open circuit fault; wherein, the first coefficient range is lower than the second coefficient range.
[0012] According to one embodiment of this application, a fault detection device includes: a signal decomposition module, configured to: perform multi-level discrete wavelet transform processing on the stator current signal of a motor to obtain wavelet coefficients of the stator current signal; a range determination module, configured to: determine a preset coefficient range in which the wavelet coefficients are located; and a fault determination module, configured to: determine the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range.
[0013] According to another embodiment of this application, a storage medium stores a computer program thereon, which, when executed by a processor of an electronic device, causes the electronic device to perform the methods described in the embodiments of this application.
[0014] According to another embodiment of this application, an electronic device may include: a memory storing a computer program; and a processor reading the computer program stored in the memory to execute the methods described in the embodiments of this application.
[0015] According to another embodiment of this application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations described in the embodiments of this application.
[0016] In this embodiment of the application, the stator current signal of the motor is subjected to multi-level discrete wavelet transform processing to obtain the wavelet coefficients of the stator current signal; the preset coefficient range in which the wavelet coefficients are located is determined; and the fault type of the inverter switching transistor corresponding to the motor is determined according to the preset coefficient range.
[0017] The fault detection method described in this embodiment of the application, by performing multi-layer discrete wavelet transform processing on the stator current signal of the motor, can obtain wavelet coefficients that jointly reflect the characteristics of the stator current signal in both the time and frequency domains. Compared to the prior art that simply judges faults based on a single time-domain or frequency-domain indicator, the magnitude of these wavelet coefficients can more accurately reflect the operating status of the inverter switching transistors of the motor. Furthermore, based on the preset coefficient range in which the wavelet coefficients fall, the fault type of the inverter switching transistors can be determined more accurately. Therefore, the fault detection method of this embodiment can effectively improve the overall accuracy of fault detection for inverter switching transistors. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart of a fault detection method according to an embodiment of this application is shown.
[0020] Figure 2 A schematic diagram of a multi-layer cascaded filter according to an embodiment of this application is shown.
[0021] Figure 3 A flowchart illustrating a fault detection process according to an embodiment of this application is shown.
[0022] Figure 4 A block diagram of a fault detection apparatus according to an embodiment of this application is shown.
[0023] Figure 5 A block diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation
[0024] The present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments provided herein are merely illustrative of the present disclosure and are not intended to limit the present disclosure. Furthermore, the embodiments provided below are some embodiments for implementing the present disclosure, and not all embodiments for implementing the present disclosure. Unless otherwise specified, the technical solutions described in the embodiments of the present disclosure can be implemented in any combination. It should be noted that, in the embodiments of this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a method or apparatus that includes a list of elements includes not only the elements expressly described, but also other elements not expressly listed, or elements inherent to implementing the method or apparatus. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other related elements (e.g., steps in the method or units in the apparatus; for example, a unit may be a portion of circuitry, a portion of a processor, a portion of a program or software, etc.) in the method or apparatus that includes that element. For example, the fault detection method provided in this disclosure includes a series of steps, but the fault detection method provided in this disclosure is not limited to the steps described. Similarly, the fault detection device provided in this disclosure includes a series of units, but the device provided in this disclosure is not limited to the units explicitly described, and may also include units that need to be set up for obtaining relevant information or processing based on the information. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. It is understood that in the specific implementation of this application, relevant data is involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0025] In motor drive systems, inverter switching transistors (such as IGBTs (Insulated Gate Bipolar Transistors) and MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors) are key components for motor speed regulation and control. Failure of these switching transistors can adversely affect the motor, drive system, and load equipment. Currently, fault detection methods typically rely on hardware detection based on overcurrent protection, threshold judgment based on stator current or voltage waveforms, and feature diagnosis based on stator current spectrum analysis to detect switching transistor faults and take appropriate measures. However, these current fault detection methods often rely on simple time-domain or frequency-domain indicators for fault diagnosis, resulting in poor accuracy and hindering the implementation of accurate countermeasures.
[0026] To address these issues, this application provides a fault detection scheme that can effectively improve the accuracy of fault detection for inverter switching transistors.
[0027] The following is a detailed description of the relevant embodiments of the fault detection scheme provided in this application.
[0028] Figure 1 A flowchart illustrating a fault detection method according to an embodiment of this application is shown. The entity executing this fault detection method may be an electronic device with processing capabilities, such as household appliances like air conditioners and heat pump equipment, as well as other control devices like mobile phones.
[0029] like Figure 1 As shown, the fault detection method may include steps S110 to S130.
[0030] Step S110: Perform multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal.
[0031] The stator current signal is the current signal of the stator of the motor. Specifically, the stator current signal of the motor can include multi-phase stator current signals. For example, for a three-phase stator, three-phase stator current signals (the stator current signal Ia of the first phase, the stator current signal Ib of the second phase, and the stator current signal Ic of the third phase) can be collected.
[0032] Multi-level Discrete Wavelet Transform (DWT) processing of the stator current signal of the motor can decompose the stator current signal at multiple levels (resolutions), realize the joint characterization of the stator current signal in the time and frequency domains, and obtain wavelet coefficients that jointly reflect the characteristics of the stator current signal in the time and frequency domains.
[0033] Each phase of the stator current signal can be processed to obtain corresponding wavelet coefficients Ci. For example, the stator current signal Ia of the first phase is processed by multi-level discrete wavelet transform to obtain the wavelet coefficients C1 of the stator current signal Ia; the stator current signal Ib of the second phase is processed by multi-level discrete wavelet transform to obtain the wavelet coefficients C2 of the stator current signal Ib; and the stator current signal Ic of the third phase is processed by multi-level discrete wavelet transform to obtain the wavelet coefficients C3 of the stator current signal Ic.
[0034] Step S120: Determine the preset coefficient range in which the wavelet coefficients fall.
[0035] Different preset coefficient ranges can be pre-defined, and the preset coefficient range in which the wavelet coefficients fall can be determined after obtaining the wavelet coefficients of the stator current signal. For example, in one example, the preset coefficient range may include a first coefficient range, a second coefficient range, and a third coefficient range. Optionally, in other examples, the preset coefficient range may include other numbers of ranges.
[0036] Step S130: Determine the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range.
[0037] By pre-assigning corresponding fault types for different preset coefficient ranges, the fault type corresponding to the aforementioned preset coefficient range of wavelet coefficients can be determined. The fault type corresponding to the preset coefficient range of wavelet coefficients can accurately reflect the current fault type of the inverter switching transistor.
[0038] The fault types of inverter switching transistors can include open circuit faults and disconnection faults. After determining the fault type of the inverter switching transistors, the motor, drive system and load equipment can be shut down, and the corresponding fault type can be reported for maintenance.
[0039] In summary, by performing multi-layer discrete wavelet transform processing on the stator current signal of the motor, wavelet coefficients that jointly reflect the characteristics of the stator current signal in both the time and frequency domains can be obtained. Compared to the prior art that simply judges faults based on a single time or frequency domain indicator, the magnitude of these wavelet coefficients can more accurately reflect the operating status of the inverter switching transistors of the motor. Furthermore, based on the preset coefficient range in which the wavelet coefficients fall, the fault type of the inverter switching transistors can be determined more accurately. Therefore, the fault detection method of this application embodiment can effectively improve the overall accuracy of inverter switching transistor fault detection.
[0040] The following description Figure 1 Further optional specific embodiments are provided for each step performed during fault detection in the example implementation.
[0041] In one embodiment, step S110, performing multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain wavelet coefficients of the stator current signal, may include: performing multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain multi-level detail coefficients; and selecting the detail coefficients located in the target layer from the multi-level detail coefficients as wavelet coefficients.
[0042] See Figure 2 Multi-layer Discrete Wavelet Transform (DWT) processing is obtained through multi-layer cascaded filters. Each layer of filters includes a low-pass filter (LPF) 210 with impulse response g[n] and a high-pass filter (HPF) 220 with impulse response h[n]. The AD sampling value of the stator current signal is passed through multi-layer cascaded filters, with each layer of filters undergoing a discrete wavelet transform (DWT) process. Finally, the approximate coefficients of the output of the low-pass filter (LPF) of the last layer and the detail coefficients of the output of the high-pass filter (HPF) of each layer can be obtained.
[0043] The initial coefficients of the low-pass filter output of each layer are downsampled by a predetermined step size (e.g., 2), and the downsampled coefficients are used as the inputs to the low-pass and high-pass filters of the next layer. The initial coefficients of the high-pass filter output of each layer are downsampled by a predetermined step size (e.g., 2) to obtain the detail coefficients of the corresponding layer. Specifically, the approximate coefficients y of the low-pass filter can be calculated according to the following formula (1). low [n], the detail coefficients y of the high-pass filter can be calculated according to the following formula (2). high [n].
[0044] (1) (2) In the above formula, i[n] represents the discrete stator current signal (such as Ia, Ib or Ic), g[n] represents the filter coefficient sequence of the low-pass filter, i[k] represents the signal value of i[n] at position k, g[nk] represents the coefficient value at position nk in the filter coefficient sequence; h[n] represents the filter coefficient sequence of the high-pass filter, h[nk] represents the coefficient value at position nk in the filter coefficient sequence.
[0045] The approximation coefficients, representing low-frequency components, reflect the overall trend of the stator current signal, while the detail coefficients, representing high-frequency components, reflect the detailed fluctuations in the stator current signal. In this embodiment, the detail coefficients of the target layer are selected as wavelet coefficients. Based on these wavelet coefficients, the fault type of the inverter switching transistor can be accurately determined. The number of target layers can be set according to actual conditions. For example, in one specific example, the number of target layers is 10, meaning the detail coefficients output by the high-pass filter of the 10th layer are selected as the wavelet coefficients used for fault identification.
[0046] Among them, multi-layer cascaded filters can be deployed in the processor or controller of electronic devices to perform multi-layer discrete wavelet transform processing.
[0047] Furthermore, in one embodiment, a multi-layer cascaded filter is deployed in the motor drive controller; the stator current signal of the motor is subjected to multi-layer discrete wavelet transform processing to obtain multi-layer detail coefficients, including: acquiring the stator current signal through the drive controller; and inputting the stator current signal into the multi-layer cascaded filter to perform discrete wavelet transform processing sequentially to obtain the detail coefficients output by the multi-layer filter.
[0048] By deploying multi-layer cascaded filters in the motor drive controller, the stator current signal can be acquired in real time and input into the multi-layer cascaded filters for discrete wavelet transform processing. This embeds the discrete wavelet transform processing into the motor drive controller, enabling real-time fault detection during motor operation, further improving the real-time performance of fault detection, and further avoiding impact on the normal control cycle of the drive system.
[0049] In some embodiments, the filter parameters of the multi-layer cascaded filters can be set and fixed.
[0050] Furthermore, in one embodiment, before the stator current signal is input into the multi-layer cascaded filter and sequentially processed by discrete wavelet transform, the method may further include: determining filter parameters according to the type of motor and / or inverter switching transistors; and setting parameters for the multi-layer cascaded filter according to the filter parameters.
[0051] Determine the filter parameters corresponding to the type of the current motor and / or inverter switching transistors, and set the parameters of the multi-layer cascaded filters according to the filter parameters. This allows for dynamic adaptation of the corresponding filter parameters based on the type of the motor and / or inverter switching transistors, which can further improve the accuracy and performance of inverter switching transistor fault detection.
[0052] The filter parameters may include wavelet basis (i.e., the filter coefficient sequence of low-pass and high-pass filters), decomposition layer number (i.e., the number of filter layers involved in the calculation; for example, if the decomposition layer number is set to 10, then the first 10 filter layers will undergo multi-layer discrete wavelet transform processing) and / or feature dimension (i.e., the dimension of the coefficients of the filter output).
[0053] Furthermore, in one embodiment, determining filter parameters based on the type of motor and / or inverter switching transistors includes: querying preset parameters corresponding to the type of motor and / or inverter switching transistors from a preset parameter adjustment table; and determining the preset parameters as filter parameters.
[0054] A preset parameter adjustment table is provided, specifying the preset parameters corresponding to different types of motor and / or inverter switching transistors. This table can be used to query and determine the preset parameters corresponding to the current type of motor and / or inverter switching transistors, which will then be used as filter parameters.
[0055] Furthermore, in one embodiment, determining filter parameters based on the type of motor and / or inverter switching transistors includes: acquiring historical fault diagnosis data corresponding to the type of motor and / or inverter switching transistors; and analyzing the historical fault diagnosis data to obtain filter parameters.
[0056] Historical fault diagnosis data can include "accuracy feedback information of fault types detected when using different filter parameters" under the current type of motor and / or inverter switching transistors. Based on the historical fault diagnosis data, filter parameters with higher accuracy than the predetermined accuracy or the highest accuracy can be selected. The filter parameters determined in this way can be used to set the parameters of multi-layer cascaded filters, which can further improve the fault detection accuracy of inverter switching transistors.
[0057] In one embodiment, step S130, determining the fault type of the inverter switch corresponding to the motor based on a preset coefficient range, may include: if the preset coefficient range is a first coefficient range, then the fault type of the inverter switch is a disconnection fault; if the preset coefficient range is a second coefficient range, then the fault type of the inverter switch is an open circuit fault; wherein, the first coefficient range is lower than the second coefficient range.
[0058] If the wavelet coefficient Ci falls within the first preset coefficient range, the inverter switch is classified as an open fault; if the wavelet coefficient falls within the second preset coefficient range, the inverter switch is classified as an open circuit fault. The first coefficient range is lower than the second coefficient range, and this implementation method can accurately detect both open and open circuit faults.
[0059] The first and second coefficient ranges can be set according to actual conditions. In one example, the first coefficient range is 2 < Ci ≤ 10, the second coefficient range is 10 < Ci, and so on. When the preset coefficient range is 0 < Ci ≤ 2, the inverter switching transistor is judged to be normal; when 2 < Ci ≤ 10, the inverter switching transistor has a disconnection fault; and when 10 < Ci, the inverter switching transistor has an open circuit fault.
[0060] If the wavelet coefficient Ci of the stator current signal of phase i is within the first coefficient range, it indicates that the bridge arm of the inverter switch corresponding to phase i has an open fault; if the wavelet coefficient Ci of the stator current signal of phase i is within the second coefficient range, it indicates that the bridge arm of the inverter switch corresponding to phase i has an open circuit fault.
[0061] To facilitate better implementation of the fault detection method provided in the embodiments of this application, the foregoing embodiments are further described below with reference to a fault detection scenario. This fault detection is performed by applying the foregoing embodiments of this application. The meanings of the terms used are the same as in the fault detection method described above, and specific implementation details can be found in the descriptions within the method embodiments. For example, Figure 3 The fault detection flowchart for this fault detection scenario is shown.
[0062] See Figure 3 The fault detection process in this fault detection scenario may include steps S310 to S350.
[0063] Step S310: Sample the three-phase stator current signals of the motor (stator current signal Ia of the first phase, stator current signal Ib of the second phase, and stator current signal Ic of the third phase). Step S320: Multi-level discrete wavelet transform processing. Specifically, the stator current signal of the motor is subjected to multi-level discrete wavelet transform processing to obtain multi-level detail coefficients.
[0064] Step S330: Select the detail coefficients of the target layer as the final wavelet coefficients Ci. In this scenario, the target layer is specifically set to 10.
[0065] Among them, the stator current signal of the i-th phase can be used to obtain the corresponding wavelet coefficient Ci. For example, the stator current signal Ia of the first phase is subjected to multi-level discrete wavelet transform processing to obtain the wavelet coefficient C1 of the stator current signal Ia; the stator current signal Ib of the second phase is subjected to multi-level discrete wavelet transform processing to obtain the wavelet coefficient C2 of the stator current signal Ib; and the stator current signal Ic of the third phase is subjected to multi-level discrete wavelet transform processing to obtain the wavelet coefficient C3 of the stator current signal Ic.
[0066] Steps S340 to S350 determine the preset coefficient range in which the wavelet coefficients are located, and determine the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range.
[0067] Specifically, in step S340, determine whether the preset coefficient range of wavelet coefficient Ci is within the third coefficient range 0 < Ci ≤ 2. If yes, it indicates that the inverter switching transistor is normal, and return to step S310. In step S350, determine whether the preset coefficient range of wavelet coefficient Ci is within the first coefficient range 2 < Ci ≤ 10. If yes, it indicates that the inverter switching transistor has an open circuit fault; if no, the preset coefficient range of wavelet coefficient Ci is within the second coefficient range 10 < Ci, indicating that the inverter switching transistor has an open circuit fault.
[0068] In this scenario, applying the aforementioned embodiments of this application for fault detection can have at least the following beneficial effects: (1) By performing multi-layer discrete wavelet transform processing on the stator current signal of the motor, wavelet coefficients that jointly reflect the characteristics of the stator current signal in the time and frequency domains can be obtained. Compared with the existing technology that simply judges faults based on a single time or frequency domain index, the magnitude of these wavelet coefficients can more accurately reflect the working status of the inverter switching transistors of the motor. Furthermore, based on the preset coefficient range of the wavelet coefficients, the fault type of the inverter switching transistors can be determined more accurately. Overall, this can effectively improve the fault detection accuracy of the inverter switching transistors.
[0069] (2) In this scenario, the detail coefficients of the target layer (10 layers) are selected as wavelet coefficients. Since the detail coefficients are high-frequency components, they can reflect the detailed fluctuations of the stator current signal. In particular, the detail coefficients of the target layer (10 layers) can usually reflect the detailed fluctuations of the stator current signal very accurately. The fault type of the inverter switching transistor can be accurately determined by these wavelet coefficients.
[0070] (3) In this scenario, the first coefficient range is set to 2 < Ci ≤ 10, the second coefficient range is 10 < Ci, and the third coefficient range is 0 < Ci ≤ 2, which correspond to the inverter switching transistor being disconnected, open-circuit faulted, and the inverter switching transistor being normal, respectively. Based on this, the disconnection fault and open-circuit fault of the inverter switching transistor can be accurately detected.
[0071] To facilitate better implementation of the fault detection method provided in the embodiments of this application, the embodiments of this application also provide a fault detection device based on the above-described fault detection method. The meanings of the terms used are the same as in the fault detection method described above, and specific implementation details can be found in the descriptions within the method embodiments. Figure 4 A block diagram of a fault detection apparatus according to an embodiment of this application is shown.
[0072] like Figure 4 As shown, the fault detection device 400 may include: a signal decomposition module 410, which can be used to: perform multi-layer discrete wavelet transform processing on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal; a range determination module 420, which can be used to: determine the preset coefficient range in which the wavelet coefficients are located; and a fault determination module 430, which can be used to: determine the fault type of the inverter switching transistor corresponding to the motor according to the preset coefficient range.
[0073] In some embodiments of this application, when performing multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal, the signal decomposition module 410 can be used to: perform multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain multi-level detail coefficients; and select the detail coefficients located in the target layer among the multi-level detail coefficients as the wavelet coefficients.
[0074] In some embodiments of this application, a multi-layer cascaded filter is deployed in the drive controller; when the stator current signal of the motor is subjected to multi-layer discrete wavelet transform processing to obtain multi-layer detail coefficients, the signal decomposition module 410 can be used to: acquire the stator current signal through the drive controller; and input the stator current signal into the multi-layer cascaded filter to perform discrete wavelet transform processing in sequence to obtain the detail coefficients output by the multi-layer filter.
[0075] In some embodiments of this application, before the stator current signal is input into the multi-layer cascaded filter for sequential discrete wavelet transform processing, the device further includes a parameter setting module for: determining filter parameters according to the type of the motor and / or the inverter switching transistor; and setting parameters of the multi-layer cascaded filter according to the filter parameters.
[0076] In some embodiments of this application, when determining the filter parameters based on the type of the motor and / or the inverter switching transistor, the parameter setting module is used to: query the preset parameters corresponding to the type of the motor and / or the inverter switching transistor from the preset parameter adjustment table; and determine the preset parameters as the filter parameters.
[0077] In some embodiments of this application, when determining the filter parameters based on the type of the motor and / or the inverter switching transistor, the parameter setting module is used to: obtain historical fault diagnosis data corresponding to the type of the motor and / or the inverter switching transistor; and analyze the historical fault diagnosis data to obtain the filter parameters.
[0078] In some embodiments of this application, when determining the fault type of the inverter switch tube corresponding to the motor according to the preset coefficient range, the fault determination module 430 can be used to: if the preset coefficient range is a first coefficient range, then the fault type of the inverter switch tube is a disconnection fault; if the preset coefficient range is a second coefficient range, then the fault type of the inverter switch tube is an open circuit fault; wherein, the first coefficient range is lower than the second coefficient range.
[0079] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0080] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 5 As shown, Figure 5 A block diagram of an electronic device according to an embodiment of this application is shown, specifically: The electronic device may include components such as a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 501 is the control center of the electronic device, connecting various parts of the computer device via various interfaces and lines. It executes software programs and / or modules stored in the memory 502, and calls data stored in the memory 502, to perform various functions of the computer device and process data. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user page, and application programs, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 501.
[0081] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.
[0082] The electronic device also includes a power supply 503 that supplies power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.
[0083] The electronic device may also include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
[0084] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 501 in the electronic device can load the executable files corresponding to the processes of one or more computer programs into the memory 502 according to the following instructions, and the processor 501 runs the computer programs stored in the memory 502, thereby realizing the various functions in the foregoing embodiments of this application.
[0085] For example, processor 501 can perform the following: perform multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal; determine the preset coefficient range in which the wavelet coefficients are located; and determine the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range.
[0086] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by a computer program, or by a computer program controlling related hardware. The computer program can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0087] Therefore, embodiments of this application also provide a storage medium storing a computer program that can be loaded by a processor to execute the steps in any of the methods provided in embodiments of this application.
[0088] The storage medium can be a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0089] Since the computer program stored in the storage medium can execute the steps of any of the methods provided in the embodiments of this application, the beneficial effects that the methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.
[0090] According to another embodiment of this application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations described in the embodiments of this application.
[0091] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.
[0092] It should be understood that this application is not limited to the embodiments described above and shown in the accompanying drawings, but various modifications and changes can be made without departing from its scope.
Claims
1. A fault detection method, characterized in that, include: The stator current signal of the motor is subjected to multi-level discrete wavelet transform processing to obtain the wavelet coefficients of the stator current signal; Determine the preset coefficient range in which the wavelet coefficients fall; The fault type of the inverter switching transistor corresponding to the motor is determined based on the preset coefficient range.
2. The method according to claim 1, characterized in that, The process of performing multi-level discrete wavelet transform on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal includes: The stator current signal of the motor is processed by multi-level discrete wavelet transform to obtain multi-level detail coefficients; The detail coefficients located in the target layer among the detail coefficients of the multilayer are selected as the wavelet coefficients.
3. The method according to claim 2, characterized in that, Deploy multi-layered cascaded filters in the drive controller; The process of performing multi-level discrete wavelet transform on the stator current signal of the motor yields multi-level detail coefficients, including: The stator current signal is acquired through the drive controller; Furthermore, the stator current signal is input into the multi-layer cascaded filter and subjected to discrete wavelet transform processing in sequence to obtain the detail coefficients output by the multi-layer filter.
4. The method according to claim 3, characterized in that, Before the stator current signal is input into the multi-layer cascaded filter and sequentially subjected to discrete wavelet transform processing, the method further includes: Determine the filter parameters based on the type of the motor and / or the inverter switching transistors; The parameters of the multi-layer cascaded filter are set according to the filter parameters.
5. The method according to claim 4, characterized in that, The step of determining the filter parameters based on the type of the motor and / or the inverter switching transistors includes: Query the preset parameters corresponding to the type of the motor and / or the inverter switching transistor from the preset parameter adjustment table; The preset parameters are determined as the filter parameters.
6. The method according to claim 4, characterized in that, The step of determining the filter parameters based on the type of the motor and / or the inverter switching transistors includes: Obtain historical fault diagnosis data corresponding to the type of the motor and / or the inverter switching transistor; The filter parameters are obtained by analyzing the historical fault diagnosis data.
7. The method according to any one of claims 1 to 6, characterized in that, The step of determining the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range includes: If the preset coefficient range is the first coefficient range, then the fault type of the inverter switching transistor is a disconnection fault. If the preset coefficient range is the second coefficient range, then the fault type of the inverter switching transistor is an open circuit fault. The range of the first coefficient is lower than the range of the second coefficient.
8. A fault detection device, characterized in that, include: The signal decomposition module is used to: perform multi-level discrete wavelet transform processing on the stator current signal of the motor to obtain the wavelet coefficients of the stator current signal; The range determination module is used to: determine the preset coefficient range in which the wavelet coefficients fall; The fault determination module is used to determine the fault type of the inverter switching transistor corresponding to the motor based on the preset coefficient range.
9. A storage medium, characterized in that, It stores a computer program that, when executed by the processor of the electronic device, causes the electronic device to perform the method described in any one of claims 1 to 7.
10. An electronic device, characterized in that, include: Memory, which stores computer programs; A processor reads a computer program stored in memory to perform the method described in any one of claims 1 to 7.