Motor fault diagnosis method, system, equipment and medium
Through the combination of the support vector machine model and the convolutional neural network, the quantitative features of the motor operating parameters are extracted, the model parameters are optimized, the problem of low accuracy of the existing motor fault diagnosis methods is solved, and high-accuracy motor fault diagnosis is achieved.
Patent Information
- Application Number
- CN202411511614.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-01-21
AI Technical Summary
The existing motor fault diagnosis methods lack effective intelligent learning and optimization mechanisms, resulting in low diagnostic accuracy.
The support vector machine model is combined with the convolutional neural network to achieve accurate diagnosis of motor faults by obtaining and preprocessing the motor operating parameters, extracting quantitative features, constructing training sets and verification sets, and optimizing the support vector machine model parameters.
The accuracy of motor fault diagnosis is improved. Through effective intelligent learning and optimization mechanisms, motor faults are diagnosed in real time, reducing the possibility of manual intervention and incorrect diagnosis.
Smart Images
Figure CN119337221A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of motor fault diagnosis, and in particular relates to a motor fault diagnosis method, system, equipment and medium. Background Art
[0002] With the continuous improvement of industrial automation, motors have become the key power source in industrial production, and their operating reliability and stability are crucial to the entire production process. However, when the motor runs for a long time, it is inevitable that the motor will have various faults due to the influence of various internal and external factors (such as load changes, environmental conditions and material fatigue). If these faults are not diagnosed and handled in time, they may cause equipment downtime, production losses and even safety accidents. Therefore, the research and application of motor fault diagnosis technology is an important topic in the industrial field.
[0003] The existing motor fault diagnosis methods mainly rely on manual experience, and use hearing and touch to identify abnormal sounds and vibrations of the motor. Although the existing motor fault diagnosis methods have improved the diagnosis efficiency to a certain extent, they lack effective intelligent learning and optimization mechanisms, resulting in low accuracy of motor fault diagnosis. Summary of the invention
[0004] The purpose of the present invention is to provide a motor fault diagnosis method, system, device and medium, which are used to solve the problem that the prior art lacks effective intelligent learning and optimization mechanisms, resulting in low accuracy of motor fault diagnosis.
[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a motor fault diagnosis method, comprising the following steps: Acquire the operating parameters of the motor, and pre-process the obtained operating parameters of the motor; Extracting quantitative features that can represent the motor state from the preprocessed motor operating parameters; Using the extracted quantitative features that can represent the motor state, determine whether the motor has a fault, and determine the type and severity of the fault to obtain a training set and a validation set; Construct a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model; The quantitative features to be diagnosed that can represent the motor state are input into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0006] A further improvement of the present invention is that the operating parameters of the motor include the vibration amplitude of the motor bearing and the motor stator, the fluctuation amplitude of the motor rotor and the temperature change of the motor winding.
[0007] A further improvement of the present invention is that the preprocessing refers to filtering and removing DC offset from the obtained operating parameters of the motor, and there is no order of precedence for the filtering and removing DC offset.
[0008] A further improvement of the present invention is that a convolutional neural network is used to extract quantitative features capable of representing the state of the motor from the preprocessed operating parameters of the motor.
[0009] A further improvement of the present invention is that the quantitative features capable of representing the motor state include time domain features, frequency domain features and high-level features. A further improvement of the present invention is that the time domain features include root mean square, standard deviation, skewness and kurtosis; the frequency domain features include spectral centroid, spectral energy, amplitude and phase of frequency components; and the high-level features include singular values and energy entropy obtained by wavelet transform.
[0010] A further improvement of the present invention is that the indicators for evaluating the performance of the support vector machine model include accuracy, recall rate and F1 score.
[0011] In a second aspect, the present invention provides a motor fault diagnosis system, including a data acquisition module, a data preprocessing module, a quantitative feature extraction module, a data set construction module, a support vector machine model training module and a motor fault diagnosis module; The data acquisition module is used to acquire the operating parameters of the motor; The data preprocessing module is used to preprocess the obtained operating parameters of the motor; The quantitative feature extraction module is used to extract quantitative features that can represent the state of the motor from the preprocessed operating parameters of the motor; The data set construction module is used to use the extracted quantitative features that can represent the motor state to determine whether the motor has a fault, and determine the type of fault and the severity of the fault, to obtain a training set and a verification set; The support vector machine model training module is used to construct a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model; The motor fault diagnosis module is used to input the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0012] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the motor fault diagnosis method introduced above when executing the computer program.
[0013] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the motor fault diagnosis method introduced above.
[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention belongs to an improved invention. Compared with the existing motor fault diagnosis method, the motor fault diagnosis method proposed by the present invention inputs the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis, and obtains the motor fault diagnosis result. It can be seen that after the support vector machine model is trained, the present invention can input the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis, and by judging whether the motor fault diagnosis result is a positive value or a negative value, the motor fault can be diagnosed in real time. Moreover, the present invention evaluates the performance of the support vector machine model based on the obtained validation set, and finally selects the optimal support vector machine model parameters, which shows that an effective optimization mechanism is adopted. Moreover, the present invention extracts the quantitative features that can represent the motor state from the pre-processed motor operating parameters, eliminates some unnecessary features, improves the accuracy of motor fault diagnosis, and effectively solves the problem that the prior art lacks effective intelligent learning and optimization mechanisms, resulting in low accuracy of motor fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 is a flow chart of the motor fault diagnosis method of the present invention; Figure 2 is a schematic diagram of a motor fault diagnosis system of the present invention; Figure 3 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION
[0016] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.
[0017] The motor fault diagnosis method proposed in the present invention inputs the quantitative features to be diagnosed that can represent the motor state into a trained support vector machine model to perform motor fault diagnosis and obtain a motor fault diagnosis result. Compared with the prior art, the present invention effectively solves the problem that the prior art lacks effective intelligent learning and optimization mechanisms, resulting in low accuracy of motor fault diagnosis.
[0018] Embodiment 1: The flow chart of the motor fault diagnosis method of the present invention is as follows Figure 1 As shown, the motor fault diagnosis method of the present invention comprises the following steps: S1. Obtaining the operating parameters of the motor and preprocessing the obtained operating parameters of the motor.
[0019] S2. Extract quantitative features that can represent the motor state from the preprocessed motor operating parameters.
[0020] S3. Use the extracted quantitative features that can represent the motor state to determine whether the motor has a fault, and determine the type of fault and the severity of the fault to obtain a training set and a verification set.
[0021] S4. Build a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model.
[0022] S5. Input the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0023] Embodiment 2: The schematic diagram of the motor fault diagnosis system of the present invention is as follows Figure 2 As shown, the motor fault diagnosis system of the present invention includes a data acquisition module, a data preprocessing module, a quantitative feature extraction module, a data set construction module, a support vector machine model training module and a motor fault diagnosis module.
[0024] The data acquisition module is used to obtain the operating parameters of the motor.
[0025] The data preprocessing module is used to preprocess the obtained operating parameters of the motor.
[0026] The quantitative feature extraction module is used to extract quantitative features that can represent the motor state from the preprocessed motor operating parameters.
[0027] The data set construction module is used to use the extracted quantitative features that can represent the motor state to determine whether the motor has a fault, and to determine the type and severity of the fault, and obtain a training set and a validation set.
[0028] The support vector machine model training module is used to construct a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model.
[0029] The motor fault diagnosis module is used to input the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0030] Embodiment 3: S1. Obtaining the operating parameters of the motor and preprocessing the obtained operating parameters of the motor.
[0031] Firstly, the operating parameters of the motor are obtained through a data acquisition card (sampling frequency of 50000 Hz), and the obtained operating parameters of the motor are preprocessed.
[0032] The operating parameters of the motor include the vibration amplitude of the motor bearing and the motor stator, the fluctuation amplitude of the motor rotor and the temperature change of the motor winding.
[0033] Preprocessing refers to filtering the obtained motor operating parameters (for example, using a bandpass filter (frequency of 0.5kHz to 10kHz) for filtering) and removing DC offset. There is no order of priority for filtering and removing DC offset.
[0034] S2. Extract quantitative features that can represent the motor state from the preprocessed motor operating parameters.
[0035] The quantitative features that can represent the motor state are extracted from the preprocessed motor operating parameters using a convolutional neural network. The quantitative features that can represent the motor state include time domain features (such as root mean square, standard deviation, skewness and kurtosis), frequency domain features (such as spectral centroid, spectral energy, amplitude and phase of frequency components) and advanced features (such as singular values and energy entropy obtained by wavelet transform).
[0036] S3. Use the extracted quantitative features that can represent the motor state to determine whether the motor has a fault, and determine the type of fault and the severity of the fault to obtain a training set and a verification set.
[0037] The extracted quantitative features (time domain features, frequency domain features and high-level features) that can represent the motor state are used to determine whether the motor has a fault, and to determine the type and severity of the fault to obtain a training set and a validation set.
[0038] S4. Build a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model.
[0039] A support vector machine model is constructed, trained based on the obtained training set, and the performance of the support vector machine model is evaluated based on the obtained validation set (the performance evaluation indicators of the support vector machine model include accuracy, recall rate and F1 score), to obtain a trained support vector machine model.
[0040] This step also uses grid search and cross validation methods to find the best parameter settings.
[0041] Assume that a feature vector x=[ , ,..., ] and the corresponding fault label y, the goal of the support vector machine is to find an optimal hyperplane , so that data points of different categories can be correctly separated, and the interval is the largest. The calculation formula is:
[0042]
[0043] in, is the constraint condition; w is the weight vector, which determines the direction of the hyperplane; b is the bias term, which determines the distance between the hyperplane and the origin; For the i The fault label of each sample is +1 or -1. For the i The feature vector of samples; ||w|| 2 is the square of the Euclidean norm of the weight vector, for Any value.
[0044] S5. Input the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0045] Finally, the quantitative features to be diagnosed that can represent the motor state are input into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0046] Specifically, this step performs motor fault diagnosis on the new sample (quantitative feature to be diagnosed that can represent the motor state) through the following formula, and the calculation formula is:
[0047] in, is the symbolic function, is the diagnostic result value, is the weight vector, is the bias term, is a characteristic vector. When the diagnosis result is a positive value, it means that the motor has no fault. When the diagnosis result is a negative value, it means that the motor has a fault.
[0048] The motor fault method proposed in the present invention can display the motor fault diagnosis result after obtaining the motor fault diagnosis result. Specifically, the motor fault diagnosis result can be displayed through a dashboard, a historical data trend chart, a fault statistics report and an alarm log, so that maintenance personnel can quickly understand the diagnostic information and take maintenance measures in time to avoid potential safety risks and unnecessary economic losses.
[0049] Specifically, the dashboard can display the current motor speed, vibration level, temperature, and fault status indicator. The historical data trend chart can show the changes of characteristic parameters over time through line graphs, helping users identify the development trend of faults. The fault statistics report summarizes the frequency and distribution of faults, providing a basis for preventive maintenance. The alarm log can display logs related to motor faults.
[0050] Embodiment 4: See also Figure 3 As shown, the present invention also provides an electronic device 100 for a motor fault diagnosis method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0051] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the motor fault diagnosis method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
[0052] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.
[0053] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a motor fault diagnosis method, and the processor 102 can execute the plurality of instructions to implement: Acquire the operating parameters of the motor, and pre-process the obtained operating parameters of the motor; Extracting quantitative features that can represent the motor state from the preprocessed motor operating parameters; Using the extracted quantitative features that can represent the motor state, determine whether the motor has a fault, and determine the type and severity of the fault to obtain a training set and a validation set; Construct a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model; The quantitative features to be diagnosed that can represent the motor state are input into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
[0054] Embodiment 5: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).
[0055] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0059] Finally, 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A motor fault diagnosis method, characterized in that: The following steps are involved: Acquire the operating parameters of the motor, and pre-process the obtained operating parameters of the motor; Extracting quantitative features that can represent the motor state from the preprocessed motor operating parameters; Using the extracted quantitative features that can represent the motor state, determine whether the motor has a fault, and determine the type and severity of the fault to obtain a training set and a validation set; Construct a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model; The quantitative features to be diagnosed that can represent the motor state are input into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
2. The motor fault diagnosis method according to claim 1, characterized in that: The operating parameters of the motor include the vibration amplitude of the motor bearing and the motor stator, the fluctuation amplitude of the motor rotor and the temperature change of the motor winding.
3. The motor fault diagnosis method according to claim 1, characterized in that: The preprocessing refers to filtering and removing DC offset from the obtained motor operating parameters, and there is no order of precedence for the filtering and removing DC offset.
4. The motor fault diagnosis method according to claim 1, characterized in that: A convolutional neural network is used to extract quantitative features that can represent the motor state from the preprocessed motor operating parameters.
5. The motor fault diagnosis method according to claim 1 or 4, characterized in that: Quantitative features that can represent the motor state include time domain features, frequency domain features and high-level features.
6. The motor fault diagnosis method according to claim 5, characterized in that: The time domain features include root mean square, standard deviation, skewness and kurtosis; the frequency domain features include spectral centroid, spectral energy, amplitude and phase of frequency components; the advanced features include singular values and energy entropy obtained by wavelet transform.
7. The motor fault diagnosis method according to claim 1, characterized in that: The indicators for evaluating the performance of the support vector machine model include accuracy, recall and F1 score.
8. A motor fault diagnosis system, characterized in that: It includes data acquisition module, data preprocessing module, quantitative feature extraction module, data set construction module, support vector machine model training module and motor fault diagnosis module; The data acquisition module is used to acquire the operating parameters of the motor; The data preprocessing module is used to preprocess the obtained operating parameters of the motor; The quantitative feature extraction module is used to extract quantitative features that can represent the state of the motor from the preprocessed operating parameters of the motor; The data set construction module is used to use the extracted quantitative features that can represent the motor state to determine whether the motor has a fault, and determine the type of fault and the severity of the fault, to obtain a training set and a verification set; The support vector machine model training module is used to construct a support vector machine model, train the support vector machine model based on the obtained training set, and evaluate the performance of the support vector machine model based on the obtained validation set to obtain a trained support vector machine model; The motor fault diagnosis module is used to input the quantitative features to be diagnosed that can represent the motor state into the trained support vector machine model to perform motor fault diagnosis and obtain the motor fault diagnosis result.
9. An electronic 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 motor fault diagnosis method according to any one of claims 1 to 7 are implemented.
10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the motor fault diagnosis method according to any one of claims 1 to 7 are implemented.