A Gear Fault Diagnosis Method Based on Multi-Scale Coarse-Grained Convolutional Neural Network

Abstract

This application relates to a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network. The method includes: obtaining original vibration data and performing preprocessing; constructing a multi-scale coarse-grained convolutional neural network fault diagnosis model based on the convolutional neural network, the model including an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer; inputting the preprocessed original vibration data into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training; after preprocessing the data to be diagnosed, inputting it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output the gear fault diagnosis result. Through the design of a multi-scale cascaded architecture and the introduction of an improved multi-scale coarse-grained layer, it is possible to effectively learn signal features on multiple time scales, automatically extract multi-scale features directly from the original vibration signal and complete fault classification, improving the classification accuracy and significantly reducing the need for manual intervention.

Description

Technical Field

[0001] The present application relates to the technical fields of artificial intelligence and mechanical engineering, and particularly to a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network. Background Art

[0002] Gears are key components indispensable in most mechanical transmission systems. In practical engineering applications, gears are extremely prone to local damage due to various factors such as fatigue, wear, overload, and corrosion, resulting in the shutdown of mechanical systems and even accidents. Therefore, in order to ensure the safety and stability of mechanical systems, improve the reliability of equipment, and reduce maintenance costs, the condition monitoring and fault diagnosis of gears have become a research field that has received increasing attention. Therefore, it is of great research value and practical significance to develop a new fault diagnosis method with high efficient adaptability and high accuracy for accurately identifying gear faults under different working conditions.

[0003] In recent years, data-driven fault diagnosis has become an important research direction in the field of mechanical intelligent fault diagnosis. Traditional data-driven fault diagnosis methods usually include two main stages: feature extraction and fault identification. Regarding data-driven intelligent fault diagnosis methods, there have been a large number of research results, such as backpropagation neural networks, extreme learning machines, support vector machines, k-nearest neighbors, hidden Markov models, and clustering analysis. However, most of these methods belong to the shallow learning process, and their fault classification is usually carried out after manual feature extraction, which makes the process complex and cumbersome. In addition, data-driven diagnosis methods often require a large amount of prior knowledge of signal processing and expert experience, which limits their adaptability and diagnostic ability. Generally speaking, these shallow learning methods have the following significant defects: (1) Feature extraction and classification are carried out independently and cannot be optimized simultaneously; (2) The extracted features highly depend on manual processing and expert experience; (3) Most shallow learning methods are only applicable to specific diagnostic scenarios and lack generality.

[0004] Therefore, in the related art, there is an urgent need for a way to achieve automatic feature extraction and fault diagnosis and improve the adaptability and robustness of the diagnosis method. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network that can achieve automatic feature extraction and fault diagnosis and improve the adaptability and robustness of the fault diagnosis method.

[0006] In a first aspect, the present application provides a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network. The method includes:

[0007] Obtain the original vibration data and perform preprocessing;

[0008] Construct a multi-scale coarse-grained convolutional neural network fault diagnosis model based on a convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of multiple stacked deep Boltzmann machine learning layers;

[0009] Input the preprocessed original vibration data into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training;

[0010] After preprocessing the data to be diagnosed, input it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output the gear fault diagnosis result.

[0011] Optionally, in an embodiment of the present application, the preprocessing includes:

[0012] Perform data segmentation on the original vibration data.

[0013] Optionally, in an embodiment of the present application, the calculation formula of the improved multi-scale coarse-grained layer is:

[0014]

[0015] Among them, is the j-th element in the improved coarse-grained time series with a length of N - τ + 1, τ is the scale factor, x(i) is the original time series, and N is the length of the original time series.

[0016] Optionally, in an embodiment of the present application, the output feature of the deep Boltzmann machine learning layer is expressed as:

[0017]

[0018] Among them, is the output feature of the j-th node in the hidden layer of the deep Boltzmann machine learning layer, σ(·) is the sigmoid activation function, is the connection weight between the i-th visible unit and the j-th hidden unit, is the j-th element in the improved coarse-grained time series with a length of N - τ + 1, is the bias term of the j-th hidden unit.

[0019] Optionally, in an embodiment of the present application, the classification layer uses a softmax classifier to calculate the probability distribution of each fault mode.

[0020] In a second aspect, the present application also provides a gear fault diagnosis device based on a multi-scale coarse-grained convolutional neural network. The device includes:

[0021] A data acquisition module, configured to acquire original vibration data and perform preprocessing;

[0022] A fault diagnosis model construction module, configured to construct a multi-scale coarse-grained convolutional neural network fault diagnosis model based on a convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of a stack of multiple deep Boltzmann machine learning layers;

[0023] A fault diagnosis model training module, configured to input the preprocessed original vibration data into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training;

[0024] A fault diagnosis module, configured to preprocess the data to be diagnosed and then input it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output a gear fault diagnosis result.

[0025] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the steps of the methods described in the above respective embodiments.

[0026] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the methods described in the above respective embodiments are implemented.

[0027] For the above gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network, first, original vibration data is acquired and preprocessed; then, a multi-scale coarse-grained convolutional neural network fault diagnosis model is constructed based on a convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of a stack of multiple deep Boltzmann machine learning layers; then, the preprocessed original vibration data is input into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training; finally, the data to be diagnosed is preprocessed and then input into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output a gear fault diagnosis result. That is to say, through the design of a multi-scale cascade architecture and the introduction of an improved multi-scale coarse-grained layer, it is possible to effectively learn signal features on multiple time scales, directly extract multi-scale features from the original vibration signal and complete fault classification, improve the classification accuracy, significantly reduce the need for manual intervention, and accurately identify fault patterns under different working conditions, having good robustness and generalization ability. Description of the Drawings

[0028] Figure 1An application environment diagram of a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network in an embodiment;

[0029] Figure 2 A schematic flow chart of a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network in an embodiment;

[0030] Figure 3 A schematic diagram of data segmentation in an embodiment;

[0031] Figure 4 A schematic structural diagram of a multi-scale coarse-grained convolutional neural network fault diagnosis model in an embodiment;

[0032] Figure 5 An analysis schematic diagram of the influence of the number of learning layers of different-scale and depth Boltzmann machines on the accuracy of diagnosis results in an embodiment;

[0033] Figure 6 A schematic overall flow chart of fault diagnosis using a multi-scale coarse-grained convolutional neural network fault diagnosis model in an embodiment;

[0034] Figure 7 A structural block diagram of a gear fault diagnosis device based on a multi-scale coarse-grained convolutional neural network in an embodiment;

[0035] Figure 8 An internal structural diagram of a computer device in an embodiment. Detailed implementation manners

[0036] In order to make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] A gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network provided by an embodiment of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or can be placed in the cloud or other network servers. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0038] In one embodiment, as Figure 2 shown, a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network is provided. Taking the application of this method to the Figure 1 server as an example, the method includes the following steps:

[0039] S201: Obtain the original vibration data and perform preprocessing.

[0040] In the embodiment of the present application, first, vibration data is obtained from a mechanical fault simulator through a data acquisition unit and monitoring software, and preprocessing is performed to make it convenient for feature extraction, and a certain proportion of samples are selected as the training set and the test set. Generally speaking, the more training data, the better the classification effect, but the time required for training will also increase accordingly. In order to achieve a balance between classification accuracy and training efficiency, the data samples of each fault mode are allocated to the training set and the test set in a ratio of 1:1. Specifically, the gear vibration data is collected by an acceleration sensor installed on the gearbox, and the sampling frequency is 5120 Hz. The gearbox consists of a pinion and a large gear, and six fault modes are covered during operation, including normal operation, pitting fault (PF) of the large gear, fracture fault (FF) of the large gear, wear fault (WF) of the pinion, pitting of the large gear and wear fault (PWF) of the pinion, and fracture of the large gear and wear fault (FWF) of the pinion.

[0041] In an embodiment of the present application, the preprocessing includes:

[0042] Perform data segmentation on the original vibration data.

[0043] In an embodiment of the present application, as Figure 3 shown, the original vibration signal is segmented into multiple equally sized sub-signals using a sliding window with overlap. In a specific application, the original gear vibration signal is segmented by a sliding window with 1024 points of overlap to obtain 100 equally sized sub-signals (each sub-signal contains 2048 data points), and each sub-signal is regarded as a sample. For each fault mode, 200 samples are obtained through data segmentation, of which 100 samples are randomly selected as the training set, and the remaining 100 samples are used as the test set. Therefore, the entire gear data set contains 1200 samples, covering six fault modes, and the number of samples in both the training set and the test set is 600.

[0044] S203: Build a multi-scale coarse-grained convolutional neural network fault diagnosis model based on the convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of multiple stacked deep Boltzmann machine learning layers.

[0045] In the embodiments of the present application, a multi-scale coarse-grained convolutional neural network fault diagnosis model is constructed with a convolutional neural network as the main body, as Figure 4 shown. The model mainly consists of three layers: (1) an improved multi-scale coarse-grained layer, (2) a multi-scale feature learning layer, and (3) a classification layer. Among them, in the improved multi-scale coarse-grained layer, an improved multi-scale coarse-grained process is applied to the original signal to obtain coarse-grained time series at different scales. In the multi-scale feature learning layer, through multiple stacked deep Boltzmann machine (DBM) learning layers, high-level and effective feature representations are automatically learned from the improved multi-scale coarse-grained time series in a parallel manner. In the classification layer, the goal is to solve the multi-class classification problem and automatically output the classification result through an appropriate classifier.

[0046] In an embodiment of the present application, the calculation formula of the improved multi-scale coarse-grained layer is:

[0047]

[0048] where is the j-th element in the improved coarse-grained time series with a length of N - τ + 1, τ is the scale factor, x(i) is the original time series, and N is the length of the original time series.

[0049] In an embodiment of the present application, after introducing the improved multi-scale coarse-grained layer, feature information estimation can be performed more accurately and reliably at different scales. In fact, the improved multi-scale coarse-grained layer is essentially a compact moving average process with the characteristic of adjacent data overlap. Compared with the traditional multi-scale transformation method, the improved multi-scale coarse-grained layer is not only simpler to implement but also has a shorter running time, thereby reducing the complexity of the model and improving the efficiency of feature learning. When the scale factor τ takes values of 1, 2, and 3, three groups of improved coarse-grained time series ( and ) are obtained from the original signal respectively to extract multi-scale feature information within different scale ranges.

[0050] In an embodiment of the present application, the output feature representation of the deep Boltzmann machine learning layer is:

[0051]

[0052] where is the output feature of the j-th node in the hidden layer of the deep Boltzmann machine learning layer, σ(·) is the sigmoid activation function, is the connection weight between the i-th visible unit and the j-th hidden unit, is the j-th element in the improved coarse-grained time series with a length of N - τ + 1, is the bias term for the j-th hidden unit.

[0053] In one embodiment of the present application, through multiple stacked deep Boltzmann machine (DBM) learning layers, high-level and effective feature representations are automatically learned from the improved multi-scale coarse-grained time series in a parallel manner. Through this parallel manner, the deep convolutional network CNN can extract deep feature information of the signal at different scales, thereby improving the performance of fault classification. Taking the scale factor τ = 3 as an example, the improved coarse-grained time series is Then the output of the j-th node in the DBM3 hidden layer is defined as:

[0054]

[0055] Similarly, the output feature representations of the deep Boltzmann machine learning layers when the scale factor τ = 1 and 2 can be obtained and Through the concatenation operation, the following high-level feature representation can finally be obtained:

[0056]

[0057] Obviously, compared with the traditional single-scale feature learning method, the above high-level feature representation contains more comprehensive and richer fault features from multiple scales, so while enhancing the feature learning ability, it also improves the ability of class discrimination.

[0058] In one embodiment of the present application, the classification layer uses a softmax classifier to calculate the probability distribution of each fault mode.

[0059] In one embodiment of the present application, the goal of the classification layer is to solve the multi-class classification problem and automatically output the classification result through an appropriate classifier. Specifically, a Softmax classifier is used to calculate the probability distribution of each fault mode. Assuming that the input sample x contains K types of health states, the probability corresponding to the j-th class can be calculated by the following formula:

[0060]

[0061] where θ represents the parameters learned from the convolutional neural network CNN, and After the above process, the classification accuracy of different fault modes can finally be obtained.

[0062] S205: Input the original vibration data after preprocessing into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training.

[0063] In the embodiments of the present application, the original vibration data after preprocessing, that is, the data of the training set and the test set, are input into the constructed multi-scale coarse-grained convolutional neural network fault diagnosis model, and the end-to-end fault diagnosis architecture based on this model is trained. Optionally, the K-nearest neighbor (KNN) algorithm can be used to optimize the model parameters.

[0064] S207: After preprocessing the data to be diagnosed, input it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model, and output the gear fault diagnosis result.

[0065] In the embodiments of the present application, after preprocessing the data to be diagnosed, such as data segmentation, etc., input it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model. The model automatically learns multi-scale features and outputs the diagnosis results of different fault modes.

[0066] In the process of multi-scale coarse-graining and multi-scale feature learning, appropriate scale factors and the number of DBMs must be preset. For example, Figure 5 As shown, it is an analysis schematic diagram of the influence of different scales and the number of layers of the deep Boltzmann machine learning layer on the accuracy of the diagnosis result. According to the prior knowledge of multi-scale analysis, it can be concluded that the more the scale factor or the number of DBMs, the higher the training time and computational complexity. Therefore, on the premise of maintaining the generality of the model, in the embodiments of the present application, the scale factor and the number of DBMs are both set to 3 to achieve a balance between accuracy and efficiency. As Figure 6 As shown, it is a schematic diagram of the overall process of fault diagnosis based on the multi-scale coarse-grained convolutional neural network fault diagnosis model.

[0067] In the above gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network, first, obtain the original vibration data and perform preprocessing; then, construct a multi-scale coarse-grained convolutional neural network fault diagnosis model based on the convolutional neural network. The model includes an improved multi-scale coarse-graining layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of multiple stacked deep Boltzmann machine learning layers; then, input the original vibration data after preprocessing into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training; finally, after preprocessing the data to be diagnosed, input it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model, and output the gear fault diagnosis result. That is to say, through the multi-scale cascade architecture design and the introduction of an improved multi-scale coarse-graining layer, it is possible to effectively learn signal features on multiple time scales, directly extract multi-scale features from the original vibration signal and complete fault classification, improve the classification accuracy, significantly reduce the need for manual intervention, and accurately identify fault modes under different working conditions, with good robustness and generalization ability.

[0068] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0069] Based on the same inventive concept, an embodiment of the present application also provides a gear fault diagnosis device based on a multi-scale coarse-grained convolutional neural network for implementing the gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the gear fault diagnosis device based on a multi-scale coarse-grained convolutional neural network provided below can refer to the limitations on the gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network in the above text, and will not be repeated here.

[0070] In one embodiment, as Figure 7 shown, a gear fault diagnosis device 700 based on a multi-scale coarse-grained convolutional neural network is provided, including: a data acquisition module 701, a fault diagnosis model construction module 703, a fault diagnosis model training module 705, and a fault diagnosis module 707, where:

[0071] The data acquisition module 701 is used to acquire the original vibration data and perform preprocessing.

[0072] The fault diagnosis model construction module 703 is used to construct a multi-scale coarse-grained convolutional neural network fault diagnosis model based on a convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of multiple stacked deep Boltzmann machine learning layers.

[0073] The fault diagnosis model training module 705 is used to input the preprocessed original vibration data into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training.

[0074] The fault diagnosis module 707 is used to preprocess the data to be diagnosed and then input it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output the gear fault diagnosis result.

[0075] In one embodiment of the present application, the data acquisition module is further configured to:

[0076] Perform data segmentation on the original vibration data.

[0077] In one embodiment of the present application, the calculation formula of the improved multi-scale coarse-graining layer is:

[0078]

[0079] Where is the j-th element in the improved coarse-grained time series with a length of N-τ+1, τ is the scale factor, x(i) is the original time series, and N is the length of the original time series.

[0080] In one embodiment of the present application, the output feature of the deep Boltzmann machine learning layer is represented as:

[0081]

[0082] Where is the output feature of the j-th node in the hidden layer of the deep Boltzmann machine learning layer, σ(·) is the sigmoid activation function, is the connection weight between the i-th visible unit and the j-th hidden unit, is the j-th element in the improved coarse-grained time series with a length of N-τ+1, is the bias term of the j-th hidden unit.

[0083] In one embodiment of the present application, the classification layer uses a softmax classifier to calculate the probability distribution of each fault mode.

[0084] Each module in the above gear fault diagnosis device based on a multi-scale coarse-grained convolutional neural network can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0085] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 8As shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0086] Those skilled in the art can understand that Figure 8 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0087] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0088] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0089] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0090] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0091] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0092] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0093] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network, characterized in that The method includes: Obtaining the original vibration data and performing preprocessing; Constructing a multi-scale coarse-grained convolutional neural network fault diagnosis model based on a convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of a stack of multiple deep Boltzmann machine learning layers; Inputting the preprocessed original vibration data into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training; After preprocessing the data to be diagnosed, inputting it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output the gear fault diagnosis result; The calculation formula of the improved multi-scale coarse-grained layer is: Among them, is the -th element in the improved coarse-grained time series with length , is the scale factor, is the original time series, is the length of the original time series. The output feature of the deep Boltzmann machine learning layer is represented as: wherein, is the output feature of the -th node in the hidden layer of the deep Boltzmann machine learning layer, is the sigmoid activation function, is the connection weight between the -th visible unit and the -th hidden unit, is the -th element in the improved coarse-grained time series with length , is the bias term of the -th hidden unit.

2. The gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network according to claim 1, wherein The preprocessing includes: Performing data segmentation on the original vibration data.

3. A gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network according to claim 1, characterized in that The classification layer uses a softmax classifier to calculate the probability distribution of each fault mode.

4. An apparatus for implementing a gear fault diagnosis method based on a multi-scale coarse-grained convolutional neural network as described in claim 1, characterized in that, The device includes: A data acquisition module for obtaining the original vibration data and performing preprocessing; A fault diagnosis model construction module for constructing a multi-scale coarse-grained convolutional neural network fault diagnosis model based on a convolutional neural network. The model includes an improved multi-scale coarse-grained layer, a multi-scale feature learning layer, and a classification layer. The multi-scale feature learning layer is composed of a stack of multiple deep Boltzmann machine learning layers; A fault diagnosis model training module for inputting the preprocessed original vibration data into the multi-scale coarse-grained convolutional neural network fault diagnosis model for model training; A fault diagnosis module for preprocessing the data to be diagnosed and then inputting it into the trained multi-scale coarse-grained convolutional neural network fault diagnosis model to output the gear fault diagnosis result.

5. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 3.

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