A Fault Diagnosis Method for Rolling Bearings Based on Feature Aligned Convolutional Neural Network

Abstract

This application discloses a rolling bearing fault diagnosis method based on feature-aligned convolutional neural network, which includes the following steps: (1) Obtain acceleration signals in various health states of rolling bearings, make the signals into equal-length samples and assign health state labels ;(2) Divide the samples into training set and test set, and set sample weights for all samples in the training set according to the weight balance principle of each category; (3) Build a feature-aligned convolutional neural network, including: feature alignment structure, feature Mapping layer and classifier, the feature alignment structure includes a single-step multi-scale convolution layer and a full-cycle maximum pooling layer; (4) Use the samples in the training set to perform weighted training on the constructed network, and use the optimal strategy to obtain the trained (5) Input the test set samples into the trained network to obtain the diagnosis result. The present invention adopts a feature alignment structure, which can enable the network model to learn more robust features, thereby improving the generalization performance of the model.

Description

technical field [0001] The invention belongs to the technical field of mechanical manufacturing, and relates to a mechanical fault diagnosis technology, in particular to a rolling bearing fault diagnosis method based on a feature-aligned convolutional neural network. Background technique [0002] Rolling bearings are one of the most widely used mechanical components in modern industrial machinery and equipment. Because they usually exist as supports for various rotating machinery, they have a very important impact on the operating status of the entire mechanical equipment. The service life distribution of rolling bearings is often very discrete, and the time of failure is difficult to predict. Therefore, real-time state monitoring and fault diagnosis of bearings are of great significance to ensure the efficient operation of mechanical equipment. [0003] With the development of computer and sensor technology, the data collected in the industrial field is growing explosively....

AI Technical Summary

Problems solved by technology

However, studies have shown that small input translations can cause a cliff-like decline in the classification accuracy of convolutional neural networks, because the network is only invariant to a certain degree of translation.

In some cases, the features mentioned by the network from the translated samples are completely distorted

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