Special Session-14

Special Session Organizers: 

Dr. Jiaxian Chen, South China University of Technology, China

Assoc. Prof. Quan Qian, University of Electronic Science and Technology of China, China

Assoc. Prof. Bingchang Hou, Chongqing University, China

Asst. Prof. Jiahui Cao, Xi’an Jiaotong University, China

Introduction and Topics:

This special session focuses on artificial intelligence-driven methods for early fault detection and remaining useful life prediction of rotating machinery. It covers advanced approaches for condition monitoring, weak fault feature extraction, health state assessment, degradation modeling, and intelligent prognostics of key rotating components and systems. The session aims to promote the development of accurate, robust, and interpretable intelligent maintenance technologies, and showcase recent advances in data-driven, physics-informed, and hybrid methods for improving the reliability, safety, and operational efficiency of rotating machinery in complex industrial environments.

Topics include but not limited to:

1. AI-driven early fault detection methods for rotating machinery
2. Remaining useful life prediction and degradation modeling of rotating components and systems
3. Multi-source sensing, signal processing, and weak feature extraction for machinery health monitoring
4. Deep learning, transfer learning, and domain adaptation for fault diagnosis and prognostics
5. Health state assessment, anomaly detection, and uncertainty quantification in prognostics and health management