Turn-to-turn short circuit fault detection method and device, electronic equipment and storage medium

Abstract

The invention provides a turn-to-turn current fault detection method, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a first current in three-phase currents of a three-phase asynchronous motor, the first current being one phase current in the three-phase currents; determining a first feature corresponding to the first current; and according to the first characteristic, performing fault detection on the three-phase asynchronous motor to obtain a fault detection result of the three-phase current of the three-phase asynchronous motor. According to the method, only any phase of current in the three-phase current needs to be obtained, feature fusion is carried out through effective value quantization processing or in combination with a fine-tuned VGG model, the calculation process is greatly simplified, and resource consumption is reduced; meanwhile, by combining multi-moment current data acquisition and multi-dimensional fault judgment, fault characteristics under different fault coefficients can be accurately captured, the problems that an existing three-phase asynchronous motor turn-to-turn short circuit fault detection method is complex in calculation and low in sensitivity are effectively solved, and the fault detection precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, and in particular to a turn-to-turn short circuit fault detection method and device, electronic equipment and storage medium. BACKGROUND

[0002] In recent years, with the development of economy and technology, asynchronous motors have been widely used in real life due to their simple structure, reliable operation, light weight and low cost. However, three-phase asynchronous motors may have some faults during use, such as bearing failure, eccentricity failure and stator failure, etc., among which turn-to-turn short circuit of motor winding coil is one of the most common faults.

[0003] In the prior art, methods such as negative sequence current, current harmonic or current vector are generally used to diagnose whether the stator winding of an asynchronous motor has a turn-to-turn short circuit fault, and these methods all have the problems of complex calculation and low detection sensitivity. SUMMARY

[0004] The main technical problem to be solved by the present application is the problem of complex calculation and low sensitivity of the turn-to-turn short circuit fault detection method of a three-phase asynchronous motor. In order to overcome the above-mentioned defects existing in the prior art, a turn-to-turn short circuit fault detection method and device, electronic equipment and storage medium are provided.

[0005] The technical solution adopted by the present application to solve its technical problem is: A turn-to-turn current fault detection method, comprising: S100. obtaining a first current in three-phase current of a three-phase asynchronous motor, the first current being a phase current in the three-phase current; S200. determining a first feature corresponding to the first current; S300. performing fault detection on the three-phase asynchronous motor according to the first feature to obtain a fault detection result of the three-phase current of the three-phase asynchronous motor.

[0006] Further, the step S200 comprises: performing effective value quantization processing on the first current at multiple time instants to obtain the first feature of the first current; The effective value quantization processing comprises: performing square processing on multiple input information and performing summation operation on the square result; using the number of input information and the summation result to obtain an effective value quantization result of the input information.

[0007] Further, the step S200 comprises: Fuse the features of the first current at multiple moments of time using a second preset model to obtain fused features; Perform effective value quantization processing on the fused features to obtain the first feature of the first current.

[0008] Further, the step S300 includes: Determine whether the first feature satisfies a corresponding relationship between a preset fault condition and an effective current value; Based on the determination result, perform fault detection on the three-phase asynchronous motor to obtain a fault detection result of the three-phase current of the three-phase asynchronous motor.

[0009] Further, the step S300 includes: Compare the first feature with a standard feature in a normal state, and obtain the fault detection result according to the comparison result.

[0010] Further, the step S300 includes: Compare the first feature and the standard feature using a first preset model to obtain the fault detection result.

[0011] Further, the first current is an A-phase current.

[0012] An inter-turn current fault detection device, characterized in that it comprises: An acquisition module configured to acquire a first current in a three-phase current of a three-phase asynchronous motor, the first current being a one-phase current in the three-phase current; A determination module configured to determine a first feature corresponding to the first current; A detection module configured to perform fault detection on the three-phase asynchronous motor according to the first feature to obtain a fault detection result of the three-phase current of the three-phase asynchronous motor.

[0013] An electronic device, comprising: A processor; A memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute a method for inter-turn current fault detection.

[0014] A computer-readable storage medium having computer program instructions stored thereon, the computer program instructions being executed by a processor to implement a method for inter-turn current fault detection.

[0015] The beneficial effects of the present application are: 1.The application only needs to obtain any one of the three-phase currents (such as the A-phase current), and through the effective value quantization processing or the combination of the fine-tuned VGG model for feature fusion, the calculation process is greatly simplified, and the resource consumption is reduced; at the same time, combined with multi-time current data acquisition and multi-dimensional fault judgment (such as TCN-LSTM model analysis and comparison with standard features), the fault characteristics under different fault coefficients (0.25, 0.55, 0.80) can be accurately captured, the missed judgment and misjudgment are reduced, the detection sensitivity is significantly improved, the problems of complex calculation and low sensitivity of the existing three-phase asynchronous motor turn-to-turn short circuit fault detection method are effectively solved, and the fault detection precision is improved.

[0016] 2.The application can not only judge whether the motor has a fault, but also can determine the fault phase (A phase, B phase, C phase) and the fault severity, provide accurate guidance for maintenance, and avoid blind troubleshooting; at the same time, based on the training data set covering multiple states and multiple fault types, the detection logic is optimized, so that the method is not excessively limited by the motor operating state, and can work stably under different working conditions. The corresponding detection device structure is clear and easy to integrate, and the method can be realized by instruction calling on various electronic devices, which is convenient for popularization and application, can effectively reduce the motor maintenance cost and improve the maintenance efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0017] The application will be further described below in combination with the drawings and examples.

[0018] Figure 1 The flowchart of the turn-to-turn current fault detection method of the embodiments of the present disclosure is shown in the figure. Figure 2 The VGG basic structure diagram is shown in the figure. Figure 3 The composition structure diagram of the first training data set according to the embodiments of the present disclosure is shown in the figure. Figure 4 The block diagram of the turn-to-turn current fault detection device according to the embodiments of the present disclosure is shown in the figure. Figure 5 The block diagram of an electronic device one according to the embodiments of the present disclosure is shown in the figure. Figure 6 The block diagram of another electronic device two according to the embodiments of the present disclosure is shown in the figure.

[0019] Explanation of figure numbers: 10, obtaining module; 20, determining module; 30, detecting module; 800, electronic device one; electronic device one; 1900, electronic device two; 802, processing component; 804, storage; 806, power component; 808, multimedia component; 810, audio component; 812, input / output interface; 814, sensor component; 816, communication component; 1922, processing component two; 1932, storage two; 1958, input / output interface two; 1950, network interface two; 1926, power component two. DETAILED DESCRIPTION

[0020] The application will be further described below in connection with specific embodiments. The illustrative embodiments of the application and the description thereto are used to explain the application and not to restrict the application.

[0021] The term "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0022] The term "and / or" used herein is only used to describe associated objects, and can represent three cases, for example, A and / or B can represent that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein represents any one of multiple or any combination of at least two of multiple, for example, at least one of A, B and C includes any one or more elements selected from the set consisting of A, B and C.

[0023] In addition, in order to better illustrate the present disclosure, a large number of specific details are given in the specific embodiments below. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some examples, methods, means, elements and circuits well known to those skilled in the art are not described in detail, in order to highlight the main idea of the present disclosure.

[0024] The execution subject of the inter-turn current fault detection method provided by the present disclosure can be an information processing device, for example, the inter-turn current fault detection method can be executed by a terminal device or a server or other processing device, wherein the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the inter-turn current fault detection method can be realized by a processor calling computer readable instructions stored in a memory.

[0025] It can be understood that the above-mentioned various method embodiments disclosed in the present application can be combined with each other to form combined embodiments without deviating from the principle logic. Due to the limited space, the present disclosure will not be repeated.

[0026] As shown in Figure 1 The present application discloses a method for detecting inter-turn current fault, comprising: S100: obtaining a first current in three-phase currents of a three-phase asynchronous motor, the first current being a phase current in the three-phase currents; In some possible embodiments, the motor parameters can be collected by a current sensor or other electrical parameter collection device to obtain a first current in three-phase stator currents of the three-phase asynchronous motor, the first current being a phase current in the three-phase currents, such as an A-phase current, or any one of B-phase and C-phase currents in other embodiments. The present disclosure does not make specific limitations thereto.

[0027] In some possible embodiments, the collected first current can be current information at a certain moment, current information in a continuous time range, or a plurality of current information obtained by using a set sampling frequency for collection.

[0028] S200: determining a first feature corresponding to the first current; After obtaining the first current, the first current can be processed to obtain a corresponding current feature as the first feature, and the first feature can represent the running state of the three-phase asynchronous motor.

[0029] S300: performing fault detection on the three-phase asynchronous motor according to the first feature to obtain a fault detection result of the three-phase currents of the three-phase asynchronous motor, the fault detection result at least including: a normal state (a state without fault), an A-phase fault (an A-phase inter-turn short circuit), a B-phase fault (a B-phase inter-turn short circuit), and a C-phase fault (a C-phase inter-turn short circuit).

[0030] In some possible embodiments, the first feature can be used for fault diagnosis by using a constructed detection model, or the comparison result with a normal feature parameter can be used for fault diagnosis to obtain a corresponding fault result.

[0031] In the embodiments of the present disclosure, any phase current information in the three-phase asynchronous motor can be used for multi-phase fault diagnosis, which is simple and convenient. In addition, the first current at multiple moments can be used for feature quantization processing in the embodiments of the present disclosure to improve the accuracy of fault diagnosis.

[0032] The present disclosure will be described in detail below in combination with specific embodiments.

[0033] First, the embodiment of the present disclosure can obtain a first current on any phase of a three-phase asynchronous motor, such as a phase A current. The first current can be current information at multiple times, and by analyzing the current information at multiple times, the false positive rate of the fault can be reduced.

[0034] In the case of obtaining the first current, the first current can be further processed to obtain a first feature. The first feature corresponding to the first current can include: performing effective value quantization processing on the first current at multiple times to obtain the first feature of the first current; the effective value quantization processing includes: performing square processing on multiple input information, and performing summation operation on the square result; using the number of input information and the summation result, an effective value quantization result of the input information is obtained. For example, in a three-phase asynchronous motor, the stator current effective value usually refers to the effective value of the current flowing through each stator winding. The formula for calculating the stator current effective value can be represented as: wherein, is the effective value of the current, is the current value of each sampling point (such as the first current), and n is the total number of sampling points.

[0035] Alternatively, in the embodiment of the present disclosure, the determination of the first feature corresponding to the first current can also include: performing feature fusion processing on the first current at multiple times using a second preset model to obtain a fusion feature; performing effective value quantization processing on the fusion feature to obtain the first feature of the first current.

[0036] That is, the embodiment of the present disclosure can use a model to perform feature fusion processing on the first current at multiple times. For example, the second preset model can be a pre-trained VGG model, such as VGG-16. It is a 16-layer network. Specifically, it is composed of 2 convolution-pooling structure layers, 3 convolution-convolution-convolution-pooling structure layers, 3 fully connected layers and 1 softmax layer. The convolution layer extracts features, the pooling layer further compresses the extracted features, reduces the parameter quantity, and effectively saves the features, and the fully connected layer expands the feature spectrum for softmax classification. All convolution layers use a 3x3 convolution kernel for same convolution, and the convolution kernel uses a sliding step of 1 to convolve the input image, ensuring that the output and input maintain the same size. All pooling layers use a 2x2 convolution kernel with a step of 2 for downsampling. After pooling, the convolution spectrum size becomes half of the original. The VGG basic structure diagram is as follows: Figure 2 .

[0037] The embodiment of the present disclosure uses the VGG-16 model, and the second preset model can perform feature fusion on the features of the first current. In addition, the embodiment of the present disclosure can also fine-tune the network parameters of the deep layer on the basis of the original network. Specifically, the embodiment of the present disclosure can fine-tune the second preset model by using the first training data set. The first training data set can include the first current in the normal state and the A-phase current under a plurality of fault coefficients. When the model is fine-tuned, the 10th, 11th and 12th layers of the network can be fixed respectively. When different layers are frozen, the loss function change process of the model is analyzed, whether the model is over-fitted or under-fitted is judged according to the loss function curve of the training set and the validation set, and the number of layers that need to be fine-tuned is determined in combination with the diagnostic accuracy, and the final model is determined.

[0038] When judging the fitting of the model to the data, when the model is under-fitted, the loss function values (Train loss and Validation loss) of the training set and the validation set are at a higher value; when both are at a lower value, the fitting of the model to the data is best; when the loss function value of the training set is smaller and the loss function value of the validation set is larger, the model is over-fitted In addition, the first training data set of the embodiment of the present disclosure can include 10 kinds of first currents, Figure 3 The composition structure of the first training data set according to the embodiment of the present disclosure is shown. The stator current of the asynchronous motor in normal operation, the stator current when the fault parameters μ=0.25, μ=0.55 and μ=0.80 are obtained, and a total of 175001*10 data samples are collected, and the obtained fault current and the current in normal operation are arranged into a data set. That is, it can include the A-phase stator current in the normal state, the A-phase stator current when the fault coefficient is 0.25, the A-phase stator current when the A, B and C phase inter-turn short circuits occur, the A-phase stator current when the fault coefficient is 0.55, the A-phase stator current when the A, B and C phase inter-turn short circuits occur, and the A-phase stator current when the fault coefficient is 0.80, the A-phase stator current when the A, B and C phase inter-turn short circuits occur. In other embodiments, the A-phase stator current when the A, B and C phase inter-turn short circuits occur under other fault coefficients can also be included, and the present disclosure does not make specific limitations thereto.

[0039] By using the first current parameter group in different states at different times to fine-tune the second preset model, the fusion and expression ability of the second preset model to the features under different states can be realized.

[0040] Based on the above configuration, the second preset model constructed can be used to perform feature fusion processing on the first currents at multiple moments to obtain corresponding fusion features. In the case of obtaining the fusion features, the effective value quantization processing described above can be further performed on the fusion features to obtain the first features of the first currents. Effective feature fusion can improve the expression capability of the features and further improve the fault detection accuracy.

[0041] In the case of obtaining the first features, the first features can be further used to perform fault detection on the three-phase asynchronous motor to obtain the fault detection results of the three-phase currents of the three-phase asynchronous motor. In the embodiments of the present disclosure, the following can be included: determining whether the first features satisfy a corresponding relationship between a preset fault condition and an effective current value; and performing fault detection on the three-phase asynchronous motor based on the determination result to obtain the fault detection results of the three-phase currents of the three-phase asynchronous motor.

[0042] In the embodiments of the present disclosure, the effective current ranges corresponding to different fault conditions (such as the A-phase current range) can be established in advance. The effective current range is the range of the effective values corresponding to the first features. First, the effective current range in the normal state can be constructed according to the first currents (or the corresponding first features) collected at multiple moments. The first features of the A-phase stator currents when the A, B, and C phase inter-turn short circuits occur with a fault coefficient of 0.25 can also be used to determine the effective current ranges in the A, B, and C phase inter-turn short circuit states when the fault coefficient is 0.25. Similarly, the effective current ranges of the A-phase stator currents when the A, B, and C phase inter-turn short circuits occur with fault coefficients of 0.55 and 0.8 can also be determined. The present disclosure does not make more specific limitations on this. Thus, when the obtained first features fall within the corresponding effective current ranges, the fault type can be determined.

[0043] In some embodiments, it can be determined whether the first features are equal to or differ from the effective values of the currents in the normal state by less than a threshold value. If yes, it is determined to be the normal state. Alternatively, the effective values of the three-phase stator currents in the normal state are equal, and if the equality does not occur, it can be initially determined that the stator has a fault. In addition, if the effective values of the three-phase stator currents of a certain phase or multiple phases are missing, it can be determined that the stator has an inter-turn circuit of a certain phase or multiple phases. Finally, when the effective values of the three-phase stator currents are not equal, it can be determined that the stator winding of the asynchronous motor has an inter-turn short circuit.

[0044] According to the relationship between the fault coefficient and the effective value of the stator current, when the inter-turn short circuit fault occurs in a certain phase, the current amplitude of the phase will significantly increase, and the stator currents of the other two phases will also be affected to some extent. The current size changes in the order of IA→IB→IC, and the details are as follows.

[0045] (a) When the A-phase winding inter-turn fault occurs, the A-phase current amplitude increases obviously, the B-phase current increases slightly, and the C-phase current decreases slightly; (b) When the B-phase winding inter-turn fault occurs, the B-phase current amplitude increases obviously, the C-phase current increases slightly, and the A-phase current decreases slightly; (c) When the C-phase winding inter-turn fault occurs, the C-phase current amplitude increases obviously, the A-phase current increases slightly, and the B-phase current decreases slightly.

[0046] In addition, the embodiment of the present disclosure can also compare the first feature with the standard feature in the normal state, and obtain the fault detection result according to the comparison result. The inter-turn short-circuit fault phase is different, and the A-phase stator current effective value change condition is also different. In practice, the A-phase stator current under different fault conditions can be taken as a feature, and compared with the A-phase stator current under normal conditions to determine whether a fault occurs. If the A-phase stator current effective value is less than the normal effective value, it is a B-phase inter-turn short-circuit fault; if the A-phase stator current effective value is slightly greater than the normal effective value, it is a C-phase inter-turn short-circuit fault; if the A-phase stator current effective value is significantly greater than the normal effective value, it is an A-phase inter-turn short-circuit fault.

[0047] In some other embodiments, the fault detection of the three-phase asynchronous motor according to the first feature to obtain the fault detection result of the three-phase current of the three-phase asynchronous motor can further include: comparing the first feature and a standard feature by using a first preset model to obtain the fault detection result.

[0048] The first preset model can be a TCN-LSTM model. TCN (Temporal Convolutional Network) is a deep learning model for processing sequence data, which captures local patterns in time series through convolutional neural networks and can be extended to capture long-distance dependencies. LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) architecture designed to solve the gradient vanishing or gradient explosion problem of standard RNN when processing long sequence data. LSTM mainly consists of cell state, hidden state, gating mechanism, weight and bias, activation function, input and output, parameter update, initialization, sequence processing and other structures. LSTM controls the flow of information by introducing gating mechanism (gates), so that the network can learn long-term dependencies.

[0049] The embodiment of the present disclosure can input the effective current value and the first feature under normal conditions into the first preset model, output the probability of 10 conditions by using the first preset model, and take the class with the highest probability as the fault detection result. The 10 conditions are normal state, and A, B and C phase turn-to-turn short circuit when the fault coefficient is 0.25, 0.55 and 0.8 respectively.

[0050] In the data set established by the present disclosure, the sample contains three-phase stator current data of the three-phase asynchronous motor under different fault degrees and different operating states, and the test set is randomly and uniformly extracted from all the data. The proposed three-phase asynchronous motor turn-to-turn short circuit fault diagnosis method is not affected by the operating state of the motor, and can accurately distinguish whether the three-phase asynchronous motor has a coil turn-to-turn short circuit fault and the different severity of the fault.

[0051] In addition, the present disclosure also provides a turn-to-turn current fault detection device, an electronic device, a computer readable storage medium and a program, which can be used to implement any of the camouflage object detection methods provided by the present disclosure. The corresponding technical solutions and descriptions are described in the method section and are not repeated here.

[0052] Reference Figure 4 As shown in the drawings, the present disclosure also discloses a turn-to-turn current fault detection device, comprising: The acquisition module 10 is configured to acquire a first current in three-phase current of a three-phase asynchronous motor, the first current being a single-phase current in the three-phase current. The determination module 20 is configured to determine a first feature corresponding to the first current. The detection module 30 is configured to perform fault detection on the three-phase asynchronous motor according to the first feature, and obtain a fault detection result of the three-phase current of the three-phase asynchronous motor.

[0053] In some embodiments, the device provided by the embodiment of the present disclosure has functions or contains modules which can be used to execute the method described in the above method embodiment. The specific implementation can refer to the description of the above method embodiment, and will not be repeated here for brevity.

[0054] The embodiment of the present disclosure also proposes a computer readable storage medium having computer program instructions stored thereon, the computer program instructions being executed by a processor to implement the above method. The computer readable storage medium can be a non-volatile computer readable storage medium.

[0055] The embodiment of the present disclosure also proposes an electronic device, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to perform the above method.

[0056] The electronic device can be provided as a terminal, a server or other forms of devices.

[0057] Reference Figure 5 As shown, for example, electronic device 800 can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, and other terminals.

[0058] Reference Figure 5 Electronic device 800 may include one or more of the following components: processing component 802, storage 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0059] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0060] Storage 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Storage 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0061] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0062] The multimedia component 808 includes a screen providing an output interface between the electronic device 1 800 and a user. In some embodiments, the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors for sensing a touch, a slide and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect duration and pressure related to the touching or sliding action. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 1 800 is in an operating mode, such as a camera mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zooming capability.

[0063] The audio component 810 is configured to output and / or input an audio signal. For example, the audio component 810 includes a microphone (MIC) configured to receive an external audio signal when the electronic device 1 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the storage 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting an audio signal.

[0064] The input / output (I / O) interface 812 provides an interface between the processing component 802 and peripheral interface modules, which can be a keypad, a click wheel, buttons, and the like. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0065] The sensor component 814 includes one or more sensors for providing various state assessments for the electronic device 1 800. For example, the sensor component 814 can detect an open / closed state of the electronic device 1 800, relative positioning of components, such as a display and a keypad of the electronic device 1 800, a change in position of the electronic device 1 800 or a component of the electronic device 1 800, presence or absence of user contact with the electronic device 1 800, an orientation or acceleration / deceleration of the electronic device 1 800, and a temperature change of the electronic device 1 800. The sensor component 814 can include a proximity sensor configured to detect presence of a nearby object without any physical touch. The sensor component 814 can further include a light sensor such as a CMOS or CCD image sensor for use in an imaging application. In some embodiments, the sensor component 814 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0066] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 1 800 and other devices. The electronic device 1 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0067] In an exemplary embodiment, the electronic device 1 800 can be implemented to perform the above-described method by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic elements for performing the above-described methods.

[0068] In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the storage 804 including computer program instructions stored therein, is also provided, which can be executed by the processor 820 of the electronic device 1 800 to complete the above-described method.

[0069] Referring to Figure 6 As shown, for example, the electronic device 2 1900 can be provided as a server. The electronic device 2 1900 includes a processing component 2 1922, which further includes one or more processors, and memory resources represented by a storage 2 1932 for storing instructions, such as application programs, executable by the processing component 2 1922. The application programs stored in the storage 2 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 2 1922 is configured to execute the instructions to perform the above-described method.

[0070] The electronic device 2 1900 can also include a power component 2 1926 configured to perform power management of the electronic device 2 1900, a wired or wireless network interface 2 1950 configured to connect the electronic device 2 1900 to a network, and an input / output (I / O) interface 2 1958. The electronic device 2 1900 can operate based on an operating system stored in the storage 2 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0071] In an example embodiment, a non-transitory computer-readable storage medium, such as storage 1932 including computer program instructions, is also provided. The computer program instructions can be executed by the processing component 1922 of the electronic device 1900 to implement the above-described methods.

[0072] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0073] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0074] The computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0075] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0076] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0077] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0078] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0079] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logic functions. It should also be noted that in some alternative implementations, the functions noted in the

[0080] The above descriptions are only the preferred embodiments of the present application, not intended to limit the present application in any form. Any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments are still within the scope of the technical solutions of the present application.

Claims

1. A method for detecting inter-turn current faults, characterized in that, include: S100. Obtain the first current among the three-phase currents of the three-phase asynchronous motor, wherein the first current is one phase current among the three-phase currents; S200. Determine the first feature corresponding to the first current; S300. Based on the first feature, perform fault detection on the three-phase asynchronous motor to obtain the fault detection result of the three-phase current of the three-phase asynchronous motor.

2. The inter-turn current fault detection method according to claim 1, characterized in that, Step S200 includes: The first current at multiple time points is subjected to effective value quantization to obtain the first characteristic of the first current; The effective value quantization process includes: The program squares multiple inputs and then sums the squared results. By using the quantity of input information and the summation result, the effective value quantification result of the input information is obtained.

3. The inter-turn current fault detection method according to claim 1, characterized in that, Step S200 includes: The first current at multiple time points is subjected to feature fusion processing using a second preset model to obtain fused features; The fusion feature is subjected to effective value quantization processing to obtain the first feature of the first current.

4. The inter-turn current fault detection method according to claim 1, characterized in that, Step S300 includes: Determine whether the first feature satisfies the correspondence between the preset fault condition and the effective current value; Based on the judgment result, fault detection is performed on the three-phase asynchronous motor to obtain the fault detection result of the three-phase current of the three-phase asynchronous motor.

5. The inter-turn current fault detection method according to claim 1, characterized in that, Step S300 includes: The first feature is compared with the standard feature under normal conditions, and the fault detection result is obtained based on the comparison result.

6. The inter-turn current fault detection method according to claim 1, characterized in that, Step S300 includes: The fault detection result is obtained by comparing the first feature and the standard feature using the first preset model.

7. The inter-turn current fault detection method according to claim 1, characterized in that, The first current is the A-phase current.

8. A detection device using the inter-turn current fault detection method according to any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire the first current in the three-phase current of the three-phase asynchronous motor, wherein the first current is one phase current in the three-phase current; A determining module is used to determine the first feature corresponding to the first current; The detection module is used to perform fault detection on the three-phase asynchronous motor based on the first feature, and obtain the fault detection result of the three-phase current of the three-phase asynchronous motor.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

Images