Adversarial Based Unsupervised Domain Adaptation for Bearing Fault Diagnosis
Abstract
In this work we build an end-to-end adversarial domain adaptation model for bearing fault diagnosis on two different datasets. Two different adversarial based unsupervised domain adaptation models are implemented, and the obtained results are compared and analyzed. This project proposed a novel feature extractor model structure for bearing vibration signal, and pseudo-label semi-supervised learning is applied with the implemented Maximum Classifier Discrepancy (MCD) model. The proposed method outperforms the original method on XJTU and CWRU bearing datasets and achieves 97.25% accuracy. The codes are available at https://github.com/Leolando/FYP.
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