Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Abstract

Abstract: Aiming at the problems of low efficiency, poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery, a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model (WDMACN) and Gram Angle Product field (GAPF) was proposed. Firstly, the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series. Secondly, the residual network is used to extract data features, and the features of the target domain without labels are pseudo-labeled, and the transferable features among the feature extractors are shared through the depth parameter, and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish. The model t through adversarial domain adaptation, thus achieving fault classification. Finally, a large number of validations were carried out on the bearing data set of Case Western Reserve University (CWRU) and the gear data. The results show that the proposed method can greatly improve the diagnostic efficiency of the model, and has good noise resistance and generalization.