KMDSAN: A novel method for cross-domain and unsupervised bearing fault diagnosis

Abstract

Cross-domain bearing fault diagnosis is a serious challenge due to the unlabeled dataset. Deep subdomain adaptation network assisted by K-means clustering algorithm (KMDSAN), a diagnosis method based on non-adversarial network and alignment of subdomain, is proposed in this paper. Taking deep subdomain adaptation network (DSAN) as basic framework is the main difference between KMDSAN and most existing methods, because DSAN emphasizes the subdomain alignment rather than global alignment. Additionally, the K-means clustering algorithm is utilized to optimize the local maximum mean discrepancy to improve the performance of DSAN. Finally, a deep network with an improved attention mechanism is designed for the feature extraction of original bearing vibration signal. In comparison to other methods, KMDSAN is concise yet highly effective, and results from two datasets related to bearing demonstrate that the proposed method achieves excellent diagnosis accuracy.