Multi-label rolling bearing fault diagnosis method based on meta-learning

Abstract

The invention discloses a multi-label rolling bearing fault diagnosis method based on meta-learning, and the method comprises the steps: constructing a multi-label fault data set of a rolling bearing,and dividing the multi-label fault data set into a training set and a test set according to fault types; extracting a time-frequency signature matrix feature T-FSMs of the fault signal; establishinga multi-label convolutional neural network model MLCML based on meta-learning; training an MLCML model by using the training set sample; verifying the trained MLCML model by using a test set sample; and carrying out fault diagnosis on the small-sample multi-label rolling bearing by utilizing the trained model. According to the method, multiple semantics contained in the rolling bearing fault sample are fully utilized, the fault diagnosis result is more accurate, meanwhile, the problem of small samples in actual fault diagnosis of the rolling bearing can be better solved through the time-frequency signature matrix characteristics and the meta-learning strategy, design is reasonable, operation is easy and convenient, and wide application value is achieved.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rotating machinery, and relates to an intelligent diagnosis method for rolling bearings, in particular to a multi-label rolling bearing fault diagnosis method based on meta-learning. Background technique [0002] Rolling bearings are key components of rotating machinery, and their health has a decisive impact on the working efficiency of the equipment. The complex structure and harsh operating conditions make rolling bearings always have a high failure rate, which will lead to huge economic losses and casualties in severe cases. Therefore, it is of great significance to the fault diagnosis of rolling bearings. Among the existing fault diagnosis technologies for rolling bearings, the intelligent diagnosis technology based on vibration signals (for example, support vector machine, artificial neural network) is one of the most widely used technologies, especially the intelligent diagnosis...

AI Technical Summary

Problems solved by technology

[0004] The above fault diagnosis method uses a single-label learning method to diagnose the single-point fault of the rolling bearing, which does not consider the multiple semantics contained in the single-point fault, and cannot output multiple labels at the same time, which is not conducive to the actual fault diagnosis of the rolling bearing.

When solving the small sample problem, the fault features that are sensitive to small sample fault diagnosis are not extracted, and the accuracy of small sample fault diagnosis needs to be further improved

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