Cross-Domain Fault Diagnosis Based on Improved Multi-Scale Fuzzy Measure Entropy and Enhanced Joint Distribution Adaptation
Aisong Qin, Hanling Mao, Kuangchi Sun, Zhengfeng Huang, Xinxin Li
Abstract
For cross-domain fault diagnosis of rotating machinery, how to reduce the discrepancy between the source and target data distributions is still a key problem. To this end, this study proposes a novel cross-domain fault diagnosis method based on improved multi-scale fuzzy measure entropy and enhanced joint distribution adaptation, aiming to address inconsistent data distribution between the source and target domains. Specifically, improved multi-scale fuzzy measure entropies are firstly developed to generate discriminative and similar features from original vibration signals. Subsequently, through embedding maximum covariance discrepancy into the existing joint distribution adaptation, an enhanced joint distribution adaptation model is utilized to reduce the distribution discrepancy between two domains. As such, more discriminative and similar features are obtained in a new subspace. The unlabeled samples in the target domain are classified by a simple statistical classifier. Finally, three public and private datasets are used to verify the effectiveness and superiority of the proposed method. Experimental results demonstrate that improved multi-scale fuzzy measure entropies have better distinguishing ability and transferable ability than several existing entropy methods, and the enhanced joint distribution adaptation is more generalized to transfer scenarios with complex data distributions.