Electrical engineering

Geology

Control engineering

Transfer of learning

Relevance (law)

Pure mathematics

Feature (linguistics)

Mathematics

Knowledge management

Transfer function

Machine learning

Engineering

Philosophy

Parallel computing

Political science

Artificial intelligence

Seismology

Fault (geology)

Transfer (computing)

Knowledge transfer

Computer science

Linguistics

Field (mathematics)

IEEE Sensors Journal

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.

Knowledge Transfer for Rotary Machine Fault Diagnosis