Cross-domain intelligent fault diagnosis of rolling bearing based on distance metric transfer learning

Abstract

Rolling bearings are present ubiquitously in mechanical equipment, timely fault diagnosis has great significance in guaranteeing the safety of mechanical operation. In real world industrial applications, the distribution of training dataset (source domain) and testing dataset (target domain) is often different and varies with operating environment, which may lead to performance degradation. In this study, a cross-domain fault diagnosis of rolling bearing method based on distance metric transfer learning (DMTL) and wavelet packet decomposition (WPD) is proposed. The Mahalanobis distance is adopted for learning the intrinsic similarity or dissimilarity between instances and learned by simultaneously maximizing the intra-class distances and minimizing the inter-class distances for target domain. The features of source domain and target domain are first extracted from original vibration signals by WPD which is a powerful tool in dealing with non-stationary signals and can provide meticulous analysis. Then, the DMTL model is adopted to eliminate the error propagation across different components, which can weaken the weight of low-quality instances and enhance the weight of high-quality samples. Finally, the k-nearest neighbor (KNN) classifier is applied to accomplish the cross-domain intelligent fault-type classification. The superiority and effectiveness of the proposed fault diagnosis model is validated by two diagnosis cases. The experimental results demonstrated that the proposed method performs better than other compared methods in recognizing various fault types and has the capability in handling the complex cross-domain adaptation scenarios.

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

“…In the field of signal decomposition, various methods have been introduced to extract fault features. Common timefrequency domain analysis methods include wavelet transform (WT) [8,9], wavelet packet decomposition (WPD) [10,11], local mean decomposition (LMD) [12,13], and empirical modal decomposition (EMD) [14,15]. While these methods have been widely applied and have shown some promising results, they also have their limitations that can affect signal processing outcomes.…”