Asynchronous motor turn-to-turn short circuit diagnosis method and system

Abstract

The invention relates to the technical field of asynchronous motor fault diagnosis, in particular to an asynchronous motor turn-to-turn short circuit diagnosis method and system, and realizes accurate detection of an asynchronous motor turn-to-turn short circuit fault by establishing a model framework and performing data acquisition, data processing, feature extraction and key parameter acquisition in combination with multiple diagnosis methods. A traditional fault diagnosis method is easily influenced by load change and external interference, the false alarm rate is high, and the sensitivity is insufficient, however, the capacity of detecting tiny faults in a complex environment is effectively improved through a signal processing technology, an intelligent algorithm and multi-sensor data fusion; moreover, the operation state of the motor can be monitored in real time, the maintenance cost and manual detection time are reduced, the diagnosis efficiency and stability are improved, the production continuity is guaranteed, and the popularization of fault diagnosis of the asynchronous motor in industrial application is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of asynchronous motor fault diagnosis, and in particular to an asynchronous motor turn-to-turn short circuit diagnosis method and system. Background Art

[0002] Asynchronous motors play a core role in the global industrial, agricultural and automated production fields. Their high efficiency and reliability are key factors in promoting the development of modern industry. According to statistics, the power consumption of asynchronous motors accounts for more than 60% of the total load of the power grid, which highlights its important position in energy consumption and industrial production. However, since motors often operate in harsh environments such as high temperature, high humidity, and dust, coupled with long-term high-load work, the stator windings of the motors are prone to failure, especially turn-to-turn short-circuit failures. The occurrence of this fault will not only cause the motor performance to deteriorate, produce abnormal sounds and vibrations, but may also cause the motor to overheat or even burn, thus affecting the normal operation of the entire production line. The occurrence of turn-to-turn short-circuit faults is usually caused by the aging of the motor insulation material, mechanical stress, electrical overload or environmental factors. In the early stage of the fault, the fault characteristics are often weak and are easily affected by factors such as motor load changes and power supply imbalance, making fault diagnosis more complicated. If these faults are not discovered and handled at an early stage, they may lead to further deterioration of the fault and even trigger a chain reaction, affecting the normal operation of other equipment, causing production interruptions and significant economic losses.

[0003] In order to ensure the stable operation of asynchronous motors, timely and accurate fault diagnosis technology is particularly important. Modern fault diagnosis technology is no longer limited to traditional electrical testing and mechanical inspection, but integrates advanced signal processing methods, intelligent algorithms and machine learning technologies to improve the accuracy and efficiency of fault detection. For example, by adopting wavelet transform to analyze the motor current signal, the fault characteristics can be effectively extracted, while the classification method based on support vector machine can accurately identify the fault mode from complex data. In addition, with the development of Internet of Things and big data technology, remote monitoring and diagnosis systems have gradually become possible, which provides new solutions for motor maintenance and fault prevention. Therefore, the early diagnosis and treatment of asynchronous motor turn-to-turn short circuit faults are not only of great significance to ensure production continuity and improve equipment reliability, but also an important measure to reduce maintenance costs, extend motor service life, ensure operator safety and promote industrial sustainable development. Future research needs to further explore more efficient, intelligent and economical fault diagnosis methods to cope with the increasingly complex industrial environment and the increasing production requirements. Stator fault is one of the main fault types of asynchronous motors, among which turn-to-turn short circuit accounts for 30% of all motor faults. As one of the most common fault types in asynchronous motors, this fault is often caused by the combined effects of multiple factors acting on the stator. Traditional motor maintenance strategies rely on regular inspections and repairs, but this method is time-consuming and may miss early diagnosis and treatment of faults. Therefore, developing an effective turn-to-turn short-circuit fault diagnosis method is crucial to improving motor reliability and reducing unexpected downtime. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is: for the inter-turn short-circuit fault of the stator winding of an asynchronous motor, by analyzing the electrical characteristics and signal characteristics, an effective diagnostic method is provided to achieve early detection and accurate diagnosis, thereby reducing the economic losses caused by the fault and improving the reliability and stability of the motor operation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for diagnosing inter-turn short circuit of an asynchronous motor, comprising: establishing a model framework and performing data collection to obtain first-stage data; processing the first-stage data to obtain second-stage data; performing feature extraction based on the second-stage data to obtain a first parameter; and using an algorithm to make a judgment based on the first parameter.

[0007] As a preferred solution of the asynchronous motor inter-turn short circuit diagnosis method described in the present invention, the establishment of the model framework and data collection include constructing a fault diagnosis model framework, using a first tool for analysis and detection, and using the model framework for data collection to obtain first-stage data.

[0008] As a preferred solution of the asynchronous motor turn-to-turn short circuit diagnosis method described in the present invention, the processing of the first-stage data includes preprocessing the first-stage data, that is, performing format conversion to obtain the second-stage data and performing further feature extraction.

[0009] As a preferred solution of the asynchronous motor inter-turn short circuit diagnosis method described in the present invention, the feature extraction includes identifying and separating important information from the second stage data to obtain a first parameter representing the motor operating state, and using the first parameter to further identify and analyze the fault.

[0010] As a preferred solution of the asynchronous motor inter-turn short circuit diagnosis method described in the present invention, the method of using an algorithm and making a judgment based on a first parameter includes analyzing the first parameter to determine the operating state, using an algorithm to improve the accuracy of the judgment, and identifying and locating the existing fault problem.

[0011] As a preferred solution of the asynchronous motor inter-turn short circuit diagnosis method described in the present invention, the first stage data includes original data collected by the model framework and the first tool, including the current, voltage and vibration of the asynchronous motor during operation.

[0012] As a preferred solution of the asynchronous motor inter-turn short circuit diagnosis method described in the present invention, wherein: the first parameter includes key operating status indicators extracted from the second stage data, which are used to indicate the health status of the motor, including current imbalance and vibration intensity during the operation of the motor.

[0013] Another object of the present invention is to provide an asynchronous motor turn-to-turn short-circuit diagnostic system, which improves the accuracy and real-time performance of asynchronous motor fault detection by integrating multiple signal processing methods such as current analysis, vibration monitoring, and axial leakage flux detection, so that the system can automatically identify faults and provide early warnings in complex working environments, thereby effectively avoiding the risks of equipment downtime and production interruptions.

[0014] In order to solve the above technical problems, the present invention provides the following technical solutions: an asynchronous motor turn-to-turn short circuit diagnosis system, comprising: a data acquisition module, a data processing module, a feature extraction module and an algorithm judgment module;

[0015] The data collection module establishes a model framework and collects data to obtain the first stage data;

[0016] The data processing module processes the first stage data to obtain the second stage data;

[0017] The feature extraction module performs feature extraction based on the second stage data to obtain the first parameter;

[0018] The algorithm judgment module uses an algorithm and makes a judgment based on the first parameter.

[0019] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the asynchronous motor turn-to-turn short circuit diagnosis method as described above are implemented.

[0020] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the asynchronous motor turn-to-turn short-circuit diagnosis method as described above.

[0021] Beneficial effects of the present invention: By integrating multiple diagnostic methods, such as diagnosis based on stator current, vibration signal analysis, and axial leakage flux monitoring, early and accurate detection of inter-turn short-circuit faults of asynchronous motors is achieved. Traditional fault diagnosis methods have the problems of high false alarm rate and insufficient sensitivity due to the influence of motor load changes and external interference. The present invention effectively improves the detection ability of minor fault characteristics in complex environments by combining signal processing technology, intelligent algorithms (such as fuzzy neural networks) and multi-sensor data fusion. At the same time, the system of the invention can monitor in real time during the operation of the motor, reducing maintenance costs, reducing the time cost of manual detection, and significantly improving the efficiency and stability of diagnosis. The implementation of this method helps to ensure production continuity, extend the service life of equipment, reduce the risk of unexpected downtime, and further promote the popularization and practicality of asynchronous motor fault diagnosis technology in industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 A flowchart of a method for diagnosing inter-turn short circuit of an asynchronous motor provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0025] Example 1, reference Figure 1, is an embodiment of the present invention, which provides an asynchronous motor turn-to-turn short circuit diagnosis method, comprising:

[0026] S1: Establish the model framework and collect data to obtain the first-stage data.

[0027] It should be noted that if Figure 1 As shown in S1, establishing a model framework and collecting data include constructing a fault diagnosis model framework, using a first tool for analysis and detection, and using the model framework to collect data to obtain first-stage data.

[0028] Furthermore, the state estimation method refers to the use of the residual signal obtained by the system state signal observed by the first tool and the signal actually detected by the system to perform fault diagnosis. By performing data analysis on the residual signal, information such as the location of the fault and the severity of the fault can be obtained; an enhanced TS fuzzy model is proposed to treat the sensor fault as a new state; the occurrence of the fault is determined by observing the residual of the stator current signal. The residual generated by the observer is robust to interference and has a certain sensitivity to faults.

[0029] In the embodiment of the present application, the first stage data is the original data collected through the model framework and the first tool, including the current, voltage and vibration of the asynchronous motor during operation. These data are the basic operating information of the motor under normal and abnormal conditions, reflecting the characteristics of the motor under different states, and can be used as the initial input for subsequent analysis; these data are collected through the combination of the model framework and the first tool, realizing structured storage and timely acquisition of data, and providing reliable data support for subsequent fault analysis and diagnosis.

[0030] In an optional embodiment, the first stage data can also be implemented in other ways, for example, it can also include other parameters related to the operation of the motor, such as temperature, to improve the accuracy of diagnosis by adding more operating data; for example, the temperature of the motor winding will rise significantly when there is a short circuit between turns. By collecting temperature data, potential short circuit problems between turns can be discovered at an early stage; this optional implementation method can be achieved by adding a temperature sensor to capture changes in the motor temperature and combine it with other parameters for comprehensive analysis.

[0031] In an optional embodiment, the first stage data can also be realized by other means, such as remote monitoring and collection through Internet of Things (IoT) technology. This method can utilize wireless transmission technology to transmit the collected data to a remote server in real time for storage and analysis, thereby facilitating centralized monitoring and management of multiple motor devices. This method is suitable for distributed devices or devices that are difficult to access, thereby improving the flexibility and real-time performance of data collection.

[0032] In an embodiment of the present application, the first tool is a state observation tool, which is used to monitor the operating status of the motor system and obtain relevant status signals. The tool can detect the operating characteristics of the motor, such as changes in physical signals such as current and voltage when faults such as turn-to-turn short circuits occur, by working in collaboration with the model framework, and generate corresponding residual signals; the residual signal is the difference signal between the normal operating state and the abnormal state of the motor. By analyzing the residual signal, fault location and severity assessment can be effectively performed; the state observation tool uses real-time data of motor operation to provide a reliable basis for detection and diagnosis of motor faults.

[0033] In an optional embodiment, the first tool can also be implemented in other ways, such as using an enhanced sensor system to replace the traditional state observation tool. The enhanced sensor system has a higher sampling frequency and stronger data processing capabilities, and can capture more detailed motor operation data, especially under high-speed operation or large load fluctuations. By using an enhanced sensor system, subtle fault characteristics can be detected earlier, thereby improving the sensitivity and accuracy of fault diagnosis.

[0034] In an optional embodiment, the first tool can also be implemented in other ways, such as using an artificial intelligence algorithm to pre-process the collected raw data. The artificial intelligence algorithm can identify abnormal patterns in the motor operation data, such as sudden current waveforms or vibration signals with abnormal frequencies, thereby marking potential faults. In this way, the workload of manual inspection can be greatly reduced, and the automation level of the diagnostic system and the accuracy of fault identification can be improved.

[0035] Example 2, reference Figure 1 , is an embodiment of the present invention, which provides an asynchronous motor turn-to-turn short circuit diagnosis method, comprising:

[0036] S2: Process the first stage data to obtain the second stage data.

[0037] It should be noted that if Figure 1 As shown in S2, processing the first stage data includes preprocessing the first stage data, that is, performing format conversion to obtain the second stage data and perform further feature extraction.

[0038] Furthermore, the second-stage data is signal characteristic data obtained by analytical models or signal processing methods, such as the residual of the state signal or the output signal analyzed by the observer. Through such data processing, the second-stage data can reflect the fault characteristics in the operation of the motor, such as current asymmetry, spectrum characteristics of the stator current and other information. Among them, in the motor, the frequency of the inter-turn fault generated near the fundamental frequency of the stator current is:

[0039]

[0040] Among them, f s is the fundamental frequency, n=0,1,2,3…, p is the number of pole pairs, s is the slip rate, and a dynamic simulation technology is used for induction motors with inter-turn short circuits in stator windings. A series of analytical thinking helps to understand the frequency components in the line current spectrum; the results show that the inter-turn short circuit will significantly increase the 150Hz frequency component in the line current spectrum. This increase is not unique, because even in healthy motors, this frequency component will exist due to magnetic material saturation and sensitivity to other symmetries; in addition, in the case of short circuit, the rotor slot harmonic components will also increase significantly, and these components also exist in healthy motors and are also sensitive to other abnormal machine conditions.

[0041] Furthermore, the method for processing the first stage data may include state estimation method and parameter estimation method. The state estimation method identifies the location and severity of the fault by analyzing the residual between the actual system signal and the observed signal, while the parameter estimation method determines whether there is a turn-to-turn short circuit fault by detecting changes in motor parameters, such as changes in stator resistance.

[0042] S3: Based on the second stage data, feature extraction is performed to obtain the first parameter.

[0043] It should be noted that if Figure 1 As shown in S3, feature extraction includes identifying and separating important information from the second stage data to obtain a first parameter representing the motor operating state, and using the first parameter to further identify and analyze the fault.

[0044] Furthermore, the first parameter includes key operating status indicators extracted from the second stage data, which are used to indicate the health status of the motor, including current imbalance and vibration intensity during the operation of the motor; the first parameter is information that can directly reflect the fault characteristics extracted from the operating data of the motor through an analytical model or signal processing method; for example, based on the imbalance analysis of the stator current, the degree of the inter-turn short circuit fault can be identified, and by observing the changing trend of the current imbalance, the development of the fault can be judged.

[0045] Furthermore, the first parameter may also include spectral characteristics, such as high-order harmonic components in motor operation, especially the increase in specific frequency harmonics of stator current under turn-to-turn short-circuit faults. The literature mentions that the 5th, 7th, and 11th harmonics in the stator current will increase significantly when there is an inter-turn short-circuit. Therefore, by detecting the amplitude changes of these specific harmonics, the presence and severity of the inter-turn short-circuit can be accurately identified; in addition, the negative-sequence current and negative-sequence voltage components can also be used as the first parameter to determine the asymmetry of the three-phase current or voltage, and further determine whether there is an inter-turn short-circuit or other unbalanced fault; by analyzing the change characteristics of the response current, a new fault characteristic component is proposed. The results show that when the fault phase is the excited phase, the steady-state value of the response current is proportional to the severity of the short circuit, and the rate of change is inversely proportional to the short-circuit resistance; in the early fault stage, even if the short-circuit resistance is large, the steady-state value of the response current will still increase with the increase in the severity of the short circuit; the fault characteristic component can effectively indicate the change of the inter-turn short-circuit fault, which is independent of the short-circuit resistance, and has high sensitivity and robustness to the inherent asymmetry of the motor. High reliability; setting the fault warning threshold of the fault feature component to 0.005 can achieve accurate diagnosis of early minor faults of asynchronous motors; the first parameter can also include parameters based on vibration characteristics. For example, in the case of a turn-to-turn short-circuit fault, the vibration amplitude and frequency of the stator and rotor will change; by analyzing the spectral characteristics of the motor vibration signal, the vibration parameters related to the fault can be extracted to further confirm the fault type and location of the motor; by utilizing these multi-dimensional parameters, the operating status of the motor can be comprehensively evaluated and potential faults can be accurately judged; when a short-circuit fault occurs in the rotor winding turn-to-turn, the fundamental frequency vibration of the rotor increases, while the stator double frequency vibration decreases; on the other hand, a rotor imbalance fault will cause the fundamental frequency vibration of the rotor to increase, and will also increase the stator double frequency vibration; the vibration characteristics caused by these two faults are different, especially the radial vibration characteristics of the stator.

[0046] Example 3, reference Figure 1 , is an embodiment of the present invention, which provides an asynchronous motor turn-to-turn short circuit diagnosis method, comprising:

[0047] S4: Using the algorithm to make a judgment based on the first parameter.

[0048] It should be noted that if Figure 1 As shown in S4, using the algorithm and making a judgment based on the first parameter includes analyzing the first parameter to determine the operating state, using the algorithm to improve the accuracy of the judgment, and identifying and locating the existing fault problem.

[0049] Furthermore, the judgment process uses the extended Kalman filter (EKF) algorithm to detect faults. The EKF algorithm is a commonly used nonlinear filtering method suitable for estimating system states in uncertain environments. Through the EKF algorithm, the first parameter in the operation of the motor can be tracked and predicted in real time. When a turn-to-turn short circuit fault occurs, the abnormal change of the parameter can be quickly detected to achieve accurate judgment of the fault. The advantage of the EKF algorithm is that it can handle random noise in nonlinear systems, thereby maintaining high detection sensitivity and robustness under complex working conditions. In addition to the EKF algorithm, the judgment process can also be combined with the negative sequence component analysis method, which uses the asymmetry of the three-phase current or voltage to determine whether the motor has a turn-to-turn short circuit or other unbalanced faults. When the motor is operating normally, the positive sequence component of the three-phase current or voltage should dominate, while in the case of a turn-to-turn short circuit, a negative sequence component will be generated. The size of the negative sequence component is related to the severity of the fault. By analyzing the negative sequence current or negative sequence voltage component in the first parameter, the existence of the fault can be determined and the degree of the fault can be preliminarily estimated. This method is relatively simple and does not require complex calculations, and is suitable for real-time monitoring scenarios.

[0050] Furthermore, in the process of using the algorithm and making judgments based on the first parameter, the Parker vector method can also be applied. This method can convert the three-phase current or voltage into a DC component, and identify the fault characteristics by observing the changes in the vector trajectory after the conversion; when a turn-to-turn short circuit occurs, the three-phase current is no longer symmetrical, and the Parker vector trajectory changes from a circle to an ellipse, and the ellipticity is correlated with the degree of the fault; by monitoring the shape change of the elliptical trajectory, the occurrence of the fault can be identified and the severity of the fault can be judged. The advantage of the Parker vector method is that it is intuitive and easy to use, and is insensitive to signal noise, which is suitable for online monitoring and early warning of motor faults; the judgment process can also use a fuzzy neural network algorithm, which combines the advantages of fuzzy logic and neural networks and can handle uncertainty and fuzzy information; the fuzzy neural network algorithm fuzzifies the fault characteristics through fuzzy logic, making the judgment of the first parameter more robust. The neural network part automatically adjusts the judgment rules through learning and training, improving the accuracy and adaptability of fault identification; in the case of complex motor operating environment and large load fluctuations, the fuzzy neural network can effectively reduce the interference of noise on the judgment results, thereby achieving high-precision fault detection; in addition, Lissajous figure analysis is also an optional algorithm for further enhancing the judgment of faults. The Lissajous figure analysis method plots the negative sequence components of the motor's voltage and current into a graph, and judges the health status of the motor by the shape of the graph; when a turn-to-turn short circuit occurs, the shape of the Lissajous figure will change, such as a tilted or distorted ellipse; by analyzing the tilt angle and shape characteristics of the graph, the existence and severity of the fault can be judged; this method is suitable for continuous status monitoring during motor operation and helps to visually judge motor faults.

[0051] Specifically, in step S4, by combining multiple algorithms for analysis, the information in the first parameter can be fully utilized to achieve accurate judgment of the inter-turn short-circuit fault of the asynchronous motor; the EKF algorithm provides powerful nonlinear processing capabilities, the negative sequence component analysis method is concise and practical, the Parker vector method is intuitive and suitable for real-time monitoring, the fuzzy neural network enhances the robustness and adaptability of the system, and the Lissajous figure analysis method provides a graphical judgment method; the combination of these algorithms can maintain a high fault identification accuracy under different working conditions and load conditions, effectively improving the overall reliability and applicability of the diagnostic system.

[0052] The above is a schematic scheme of an asynchronous motor turn-to-turn short-circuit diagnosis method of this embodiment. It should be noted that the technical scheme of the system of the asynchronous motor turn-to-turn short-circuit diagnosis method and the technical scheme of the asynchronous motor turn-to-turn short-circuit diagnosis method described above belong to the same concept, and the details of the technical scheme of the asynchronous motor turn-to-turn short-circuit diagnosis system not described in detail in this embodiment can all be referred to the description of the technical scheme of the asynchronous motor turn-to-turn short-circuit diagnosis method described above.

[0053] Embodiment 4 is an embodiment of the present invention, which provides an asynchronous motor turn-to-turn short circuit diagnosis system, including: a data acquisition module, a data processing module, a feature extraction module and an algorithm judgment module;

[0054] The data collection module establishes a model framework and collects data to obtain the first stage data;

[0055] The data processing module processes the first stage data to obtain the second stage data;

[0056] The feature extraction module performs feature extraction based on the second stage data to obtain the first parameter;

[0057] The algorithm judgment module uses the algorithm and judges according to the first parameter

[0058] This embodiment further provides a computing device, which is applicable to the case of the method for diagnosing short circuit between turns of an asynchronous motor, and includes:

[0059] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the asynchronous motor inter-turn short circuit diagnosis method proposed in the above embodiment.

[0060] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for diagnosing inter-turn short circuit of an asynchronous motor proposed in the above embodiment is implemented.

[0061] The storage medium proposed in this embodiment and the asynchronous motor turn-to-turn short circuit diagnosis method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0062] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0064] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for diagnosing short-circuit between turns of an asynchronous motor, characterized in that: include: Establish the model framework and collect data to obtain the first phase data; Process the first stage data to obtain the second stage data; Based on the second stage data, feature extraction is performed to obtain the first parameter; An algorithm is used to make a determination based on the first parameter.

2. The method for diagnosing an asynchronous motor turn-to-turn short circuit according to claim 1, characterized in that: The establishing of the model framework and data collection includes constructing a fault diagnosis model framework, using a first tool for analysis and detection, and using the model framework for data collection to obtain first-stage data.

3. The method for diagnosing an asynchronous motor turn-to-turn short circuit according to claim 2, characterized in that: The processing of the first stage data includes preprocessing the first stage data, that is, performing format conversion to obtain the second stage data and performing further feature extraction.

4. The method for diagnosing an asynchronous motor turn-to-turn short circuit according to claim 3, characterized in that: The feature extraction includes identifying and separating important information from the second stage data to obtain a first parameter representing the motor operation state, and using the first parameter to further identify and analyze the fault.

5. The method for diagnosing an asynchronous motor turn-to-turn short circuit according to claim 4, characterized in that: The method of using an algorithm and making a judgment based on the first parameter includes analyzing the first parameter to determine the operating state, using the algorithm to improve the accuracy of the judgment, and identifying and locating the existing fault problem.

6. The method for diagnosing an asynchronous motor turn-to-turn short circuit according to claim 5, characterized in that: The first stage data includes raw data collected through the model framework and the first tool, including current, voltage and vibration of the asynchronous motor during operation.

7. The method for diagnosing an asynchronous motor turn-to-turn short circuit according to claim 6, characterized in that: The first parameters include key operating status indicators extracted from the second stage data, which are used to indicate the health status of the motor, including current imbalance and vibration intensity during the operation of the motor.

8. A system for diagnosing turn-to-turn short circuit of an asynchronous motor according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, data processing module, feature extraction module and algorithm judgment module; The data collection module establishes a model framework and collects data to obtain the first stage data; The data processing module processes the first stage data to obtain the second stage data; The feature extraction module performs feature extraction based on the second stage data to obtain the first parameter; The algorithm judgment module uses an algorithm and makes a judgment based on the first parameter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the asynchronous motor turn-to-turn short circuit diagnosis method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the asynchronous motor turn-to-turn short circuit diagnosis method according to any one of claims 1 to 7 are implemented.

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