Rotating machinery fault diagnosis method based on one-dimensional deep residual convolutional neural network

Abstract

The invention discloses a rotating machinery fault diagnosis method based on a one-dimensional deep residual convolutional neural network. First, the accumulated one-dimensional residual module enables the network to learn deeper and more abstract fault features of training samples; then, Use the Adam optimization algorithm to optimize all hyperparameters, complete the extraction of deep-level features and fault classification, and obtain a rotating machinery fault diagnosis system model based on a one-dimensional deep residual convolutional neural network; finally, input the test samples into the trained In the fault diagnosis model of the system, deep features are automatically extracted to diagnose the health status of rotating machinery.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rotating machinery, and more specifically, relates to a fault diagnosis method of rotating machinery based on a one-dimensional deep residual convolutional neural network. Background technique [0002] Rotating machinery is the most widely used component in industrial equipment. Once a failure occurs, it will inevitably lead to equipment failure, resulting in economic losses and even safety accidents. Therefore, it is of great significance to carry out fault diagnosis on rotating machinery. [0003] Traditional intelligent fault diagnosis methods mainly include data acquisition, feature extraction and fault identification. Among them, feature extraction and fault identification are the two most important steps, which have a direct impact on the accuracy of fault diagnosis results. However, traditional intelligent fault diagnosis methods have the following disadvantages: 1) The diagnos...

AI Technical Summary

Problems solved by technology

However, traditional intelligent fault diagnosis methods have the following disadvantages: 1) The diagnostic performance depends on the design of the feature extraction method by domain experts, and for each specific diagnostic task, the feature extraction method must be redesigned, so it is time-consuming and labor-intensive; 2 ) Manually extracted features cannot guarantee to fully represent the complex dynamic characteristics of rotating machinery; 3) Fault identification methods such as support vector machines, k-nearest neighbors, random forests, and naive Bayes all use shallow learning models, which are difficult Learn enough features to easily cause misjudgment

However, the published related methods almost all have the problems of low accuracy and poor domain adaptability under complex working conditions

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