Intelligent fault diagnosis method based on asymmetric domain adversarial self-adaptive model

Abstract

The invention provides an intelligent fault diagnosis method based on an asymmetric domain adversarial self-adaptive model. The problems that existing unsupervised domain adaptive adversarial training is difficult, and target domain unlabeled data demand is large are solved. Firstly, a vibration signal acquired by a sensor is converted into a time-frequency map, and domain invariant fault features are obtained from the time-frequency map by using step-by-step domain confrontation training; and meanwhile, the lightweight network architecture and the Wasserstein i system are integrated into the adversarial network, so that the training cost of a network model is greatly reduced, the dependence on target domain data is reduced, and the adversarial migration model still keeps good performance in a small sample scene.

Description

technical field [0001] The invention relates to fault pattern recognition of rolling bearings under variable working conditions, in particular to an intelligent fault diagnosis method based on asymmetric domain confrontation self-adaptation for scenarios where fault sample data is scarce. Background technique [0002] With the advent of the era of big data, intelligent fault diagnosis based on deep learning algorithms has gradually become the mainstream method in the field of equipment fault diagnosis and maintenance and management of equipment status and health. The deep learning algorithm extracts equipment fault information from a large amount of state data, and at the same time relies on its own powerful representation learning ability to realize the end-to-end fault diagnosis process. [0003] However, in the actual industrial production process, 1) On the one hand, the equipment is in normal operation for a long time, and it is difficult for the staff to obtain a large...

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Problems solved by technology

[0007] Aiming at a series of problems in the above-mentioned prior art, the object of the present invention is an intelligent fault diagnosis method based on asymmetric domain confrontation self-adaptation, which solves the shortcomings of traditional diagnosis methods and improves the stability of intelligent diagnosis in industrial applications and reliability

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