Instance Weighting-Based Partial Domain Adaptation for Intelligent Fault Diagnosis of Rotating Machinery
Yuqing Li, Yunjia Dong, Minqiang Xu, Pengpeng Liu, Rixin Wang
Abstract
The cross-domain fault diagnosis problem based on deep domain adaptation (deep DA) has gained great attention in recent years. However, a required but not easily satisfied assumption in many researches that the label space of the source domain and the target domain should be identical limits their applications in practice. In industrial reality, the label space of the target domain may be only a subset of that of the source domain, which is defined as partial DA diagnosis scenario. Focusing on this scenario, this paper proposes a novel diagnosis method named instance weighted mean maximum discrepancy (IWMMD). A new weighting mechanism inspired by instance discrimination is designed to realize DA on shared label space between domains. Also, discrimination structure enhancement for both domains is introduced to encourage better classification ability and safer domain alignment. The effectiveness of IWMMD is verified by two datasets. In the gear dataset, the diagnosis accuracy is 89.65% with a 5.11% improvement. In the bearing dataset, the diagnosis accuracy is 96.28% with a 4.46% improvement. The results and analysis show that the proposed method can reduce the negative transfer effects caused by outlier class samples in source domain and learn a more separable discrimination structure, which is effective in both no-partial and partial diagnosis scenarios and time-efficient.