Mechanical failure migration diagnosis method and system based on adversarial learning

Abstract

The invention discloses a mechanical failure migration diagnosis method and system based on adversarial learning. The method comprises the following steps: acquiring and analyzing original signals ofmechanical failure under different working conditions to generate a labeled source domain training dataset, an unlabelled source domain training dataset and a target domain test dataset under different working conditions; training a deep convolutional neutral network model according to the labeled source domain training dataset and a back propagation algorithm to generate a failure diagnosis model; training the failure diagnosis model according to the unlabelled source domain training dataset and the target domain test dataset; fine adjusting the trained failure diagnosis model according to the labeled source domain training dataset and the back propagation algorithm; inputting the unlabelled target domain test dataset into the fine adjusted failure diagnosis model, and outputting the failure category of a to-be-tested sample. By means of the method, the domain invariant feature is obtained with the adversarial learning method, migration among different domains is realized, and intelligent diagnosis of mechanical failure under variable working conditions is realized.

Description

technical field [0001] The invention relates to the technical field of mechanical equipment fault diagnosis, in particular to a mechanical fault migration diagnosis method and system based on adversarial learning. Background technique [0002] With the gradual development of industrial technology, the demand for industrial equipment continues to rise. Nowadays, the scale of industrial system integration is getting larger and larger, the structure of individual equipment is becoming more and more complex, and the degree of coupling between different devices in the same system is also increasing. On the one hand, these factors lay a solid foundation for mechanical equipment to achieve complex behavior, but at the same time lead to a significant increase in the probability of failure of the entire system. [0003] Existing industrial systems usually operate continuously and stably for a long time, and the frequency of failures is low. However, once a failure occurs, the failure...

AI Technical Summary

Problems solved by technology

[0006] At present, the problem of migration fault diagnosis is still in the preliminary stage, and there are still many technical difficulties that need in-depth research to make breakthroughs

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