Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery

Abstract

Rotating machinery plays an essential part in many engineering fields. It needs prompt solutions to the prognosis and health management to ensure the system reliability. Facilitated by big data and artificial intelligence, intelligent fault diagnosis provides a new approach. As for the insufficient faulty data and complex conditions, deep transfer learning (DTL) presents a possible approach for cross-domain and cross-machine diagnosis. The published reviews thus far mainly emphasize on the analysis of fault diagnosis based on common classes of DTL or industrial application scenarios. This review concentrates on the applications of DTL in rotating machinery. Moreover, present relevant reviews were mainly till the end of 2021. The latest researches are analyzed from then until now. A special main line based on input types is chosen that distinguishes it from other reviews. From this perspective, it is therefore valuable to comprehensively investigate the fault diagnosis of rotating machinery. This survey firstly outlines the fundamental principle and conventional categories of DTL. The primary applications of DTL in fault diagnosis of rotating machinery are then summarized, and more than 100 relative studies have been analyzed. The special perspective of input types is selected and evaluated, including one-dimensional and two-dimensional, on the DTL framework as applied to the rotary machines discussed. Finally, the existing challenges are pointed out and potential future trends of DTL are prospected. This review helps readers to understand the research status and development trends of transfer intelligent fault diagnosis. It serves to the innovative exploration from multiple different scales.