A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines

Fault diagnosis plays an indispensable role in prognostics and health management of rotating machines. In recent years, intelligent fault diagnosis methods based on domain adaptation technology have attracted the attention of researchers. However, a more extensive application scenario of fault diagnosis - partial domain adaptation (PDA) - has not been well-resolved. In this article, for the first time, a novel stacked auto-encoder based partial adversarial domain adaptation (SPADA) model is proposed to solve the fault diagnosis problem in PDA situations. Two deep stack auto-encoders are first designed to extract representative features from the training data (source domain) and test data (target domain), respectively. Then, a weighted classifier based on Softmax is used to weight the features from the source and target domains. Meanwhile, another domain discriminator and label predictor using the Softmax classifier are adopted to simultaneously implement domain adaptation and fault diagnosis. Comprehensive analysis is performed on real data to test the performance of the SPADA model and detailed comparisons are provided; the extensive experimental results show that the diagnosis performance of SPADA outperforms the existing deep learning and domain adaptation methods in dealing with the PDA problem.

Automated summary

This research introduces a novel stacked auto-encoder based partial adversarial domain adaptation (SPADA) model to address the fault diagnosis problem in partial domain adaptation (PDA) scenarios. Through comprehensive analysis and comparison on real data, experimental results demonstrate that SPADA outperforms existing deep learning and domain adaptation methods in handling the PDA problem with better diagnostic performance.