Cross-working-condition adversarial diagnosis method for bearing

Abstract

The invention discloses a cross-working-condition adversarial diagnosis method for a bearing, and the method comprises the steps: collecting the vibration data of a bearing in an operation state, segmenting the vibration data to generate signal samples, and dividing the samples into a test set for testing and a training set for training; constructing a training module comprising a feature extractor for extracting signal features, a classifier for classifying bearing faults and a discriminator for distinguishing working conditions and faults of the features, using the training module for training based on the training set, and updating the feature extraction parameters and the classifier parameters by utilizing a BP method according to a loss function; fixing the feature extractor parameters, and updating discriminator parameters by using the loss function; fixing discriminator parameters, and updating feature extractor parameters by using an adversarial loss function; and constructing a test module based on the updated classifier and the feature extractor, and inputting a test set and / or a target domain working condition sample into the test module to perform fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of intelligent diagnosis of bearing faults, in particular to a confrontation diagnosis method for bearings across working conditions. Background technique [0002] Rolling bearings are important key parts in rotating machinery, and the safety of the bearings inside is directly related to the normal operation of the machinery. Traditional intelligent diagnosis relies on experts to extract features and complex signal processing. Due to the high reliance on expert knowledge, traditional methods cannot be widely used. [0003] Traditional artificial intelligence fault diagnosis methods based on deep learning rely on samples from new working conditions to participate in training in the field of variable working conditions (the speed and load of the training set are different from the speed or load of the test set), and retraining is required when new working conditions occur in actual operation , wasting a lot o...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the problems existing in the prior art, the present invention proposes a confrontation diagnosis method for bearings across working conditions, which does not require manual feature extraction and reduces dependence on expert knowledge, while overcoming the shortcomings of traditional artificial intelligence semi-supervised variable working conditions training, That is, unlabeled target field data is required for training, and retraining is required once the target field changes

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