Applications of Domain Generalization to Machine Fault Diagnosis: A Survey

In actual industrial scenarios, the variation of operating conditions, the existence of data noise, and failure of measurement equipment will inevitably affect the distribution of perceptive data. Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed, and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data. Domain generalization (DG) aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training. The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained. In this article, for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years. Specifically, we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations. Then, the recent trend of DG for fault diagnosis is also analyzed. Finally, the existing problems and future prospect is performed.