Knowledge transfer in fault diagnosis of rotary machines

Abstract

Data-driven fault diagnosis has prevailed in machine condition monitoring in the past decades. However, traditional machine- and deep-learning-based fault diagnosis methods assumed that the source and target data share the same distribution and ignored knowledge transfer in dynamic working environments. In recent years, knowledge transfer approaches have been developed and have shown promising results in intelligent fault diagnosis and health management of rotary machines. This paper presents a comprehensive review of knowledge transfer approaches and their applications in fault diagnosis of rotary machines. A problem-oriented taxonomy of knowledge transfer in fault diagnosis is proposed. The knowledge transfer paradigms, approaches, and applications are categorised and analysed. Future research challenges and directions are explored from data, modelling, and application perspectives.