Semi-supervised mechanical fault diagnosis method based on adaptive migration neural network

Abstract

The invention discloses a semi-supervised mechanical fault diagnosis method based on an adaptive migration neural network, and the method comprises the steps: firstly obtaining a plurality of source domain fault data sets composed of source domain fault training samples and corresponding tags, and a plurality of target domain fault data sets composed of target domain fault data without tags, wherein the target domain fault data is divided into a target domain fault training sample and target domain fault test data; normalizing the data; constructing an adaptive migration neural network diagnosis model, supervising the training model and constructing a classifier loss function by using the source domain fault data set, constructing a classifier discrimination loss function, and performing adversarial training on the feature extractor and the classifier by using the target domain fault training sample; inputting the target domain fault test data into the trained model, and summing and averaging the two output probability values to obtain a final classification diagnosis result. The method can improve the discrimination capability of the fault data of the target domain, and effectively improves the intelligent fault diagnosis task under the actual variable working condition.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a semi-supervised mechanical fault diagnosis method based on an adaptive migration neural network. Background technique [0002] Rotating machinery is widely used in aerospace, automobile manufacturing, rail transit, wind power generation and other important engineering fields of national economy and people's livelihood, and plays a pivotal role in national economic production. Carry out condition monitoring and diagnosis of mechanical equipment, detect, diagnose and predict possible failures, so as to "prevent them before they happen", and ensure the reliable, continuous and stable operation of machinery, reduce economic losses and operating costs, and avoid major accidents , has very important practical needs and practical significance. [0003] The diagnosis method based on machine learning does not need to establish a dynamic model of complex components or...

AI Technical Summary

Problems solved by technology

However, it is necessary to re-collect labeled fault data for each diagnosis, which is not easy to achieve in practical applications. Therefore, it is necessary to find an effective learning method to improve the classification performance of deep neural networks under variable working conditions and improve the accuracy of faults. Diagnostic reliability and generalizability

Images