Knowledge Transfer for Rotary Machine Fault Diagnosis

Abstract

This paper intends to provide an overview on recent development of knowledge transfer for rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After brief introduction of parameter-based, instance-based, feature-based and relevance-based knowledge transfer, the applications of knowledge transfer in RMFD are summarized from four categories: transfer between multiple working conditions, transfer between multiple locations, transfer between multiple machines, and transfer between multiple fault types. Case studies on four datasets including gears, bearing, and motor faults verified effectiveness of knowledge transfer on improving diagnostic accuracy. Meanwhile, research trends on transfer learning in the field of RMFD are discussed.