Image encoding-based bearing fault diagnosis: Review and challenges for high-speed trains

High-speed trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the filed of intelligent operation and maintenance. • This paper provides a systematic review of image encoding-based bearing fault diagnosis methods of HSTs. • This paper classifies image encoding-based bearing fault diagnosis methods into monomodal image conversion and multimodal image fusion approaches. • Through critical analysis of existing research, it identifies key knowledge gaps and persistent technical challenges, and proposes future research directions.