A Novel Unsupervised Clustering and Domain Adaptation Framework for Rotating Machinery Fault Diagnosis

Abstract

Deep learning-based fault diagnosis methods require a large number of labeled datasets. However, considering the changing operating conditions, it is impractical to obtain labeled datasets for all cases. Therefore, this study proposes a new unsupervised clustering and domain adaptation framework to circumvent data deficiency and domain issues. The proposed framework comprises two steps: unsupervised clustering and domain adaptation. In the unsupervised clustering, an expectation-maximization adversarial autoencoder, which combines an expectation-maximization algorithm with an adversarial autoencoder, is used for feature extraction and subspace mapping. Subsequently, the mapped features are clustered using a Gaussian mixture model. In the domain adaptation, a domain synchronization that is based on the symmetric Kullback-Leibler divergence metric is used to infer the relationship between the source and target domain clusters. The experiments on two rolling-element-bearing datasets validate the effectiveness of our method. Specifically, our method performs domain adaptation without retraining, which is promising for real industrial applications.