Bearing fault mode diagnosis method and system for small sample data set

Abstract

The invention relates to a bearing fault mode diagnosis method and system for a small sample data set. The method comprises the following steps: 1) collecting vibration signal data of a bearing underdifferent working conditions of different equipment through an acceleration sensor; 2) preprocessing the signal, and converting the original one-dimensional signal into a two-dimensional signal through a continuous wavelet transform algorithm to form image data; 3) constructing a bearing fault diagnosis model framework based on a convolutional neural network, which comprises a coding module and amatching module, randomly sampling from the image data, and constructing learning tasks of a plurality of small sample sets so as to train the model; and 4) collecting a vibration signal of a target bearing, and diagnosing the bearing fault mode according to the preprocessing method and the bearing fault diagnosis model. Compared with the prior art, the method combines deep learning and meta-learning algorithms, and can improve the diagnosis precision under the condition of insufficient data volume.

Description

technical field [0001] The invention relates to the technical field of high-end equipment structure fault diagnosis, in particular to a bearing fault mode diagnosis method and system for small sample data sets. Background technique [0002] Rolling bearings, as key components in modern high-end equipment, have weak ability to withstand impact and are extremely prone to fatigue and damage. Once a failure occurs, it will have a huge negative impact on the entire production process, not only causing serious economic losses, but even endangering the lives of relevant personnel. Therefore, it is extremely necessary to conduct research on fault diagnosis technology for rolling bearings, and it is of great significance for the predictive maintenance of high-end equipment. [0003] At present, there are many fault diagnosis techniques based on machine learning and even deep learning, such as support vector machines, random forests, gradient boosting trees, recurrent neural networks...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a bearing failure mode diagnosis method and system for small sample data sets in order to overcome the above-mentioned defects in the prior art. The method combines deep learning and meta-learning algorithms to improve diagnosis in the case of few samples In addition, it can alleviate the performance degradation problem caused by the inconsistency of the training set and the test set sample distribution to a certain extent.

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