This book presents data-driven methods for unsupervised anomaly detection and few-shot fault diagnosis in complex industrial processes. It is intended for graduate students, academic researchers, and practicing engineers in industrial engineering, automation, and intelligent manufacturing. Complex industrial processes often exhibit strong multivariable coupling, nonlinear dynamics, and long-term temporal dependencies. These characteristics make traditional model-based and rule-based monitoring approaches difficult to apply, particularly when accurate physical models are unavailable and labeled fault data are limited. To address these challenges, the book focuses on two closely related topics: multivariate time-series modeling for unsupervised anomaly detection and meta-learning for few-shot fault diagnosis. The proposed methods are developed for industrial monitoring scenarios and aim to support reliable anomaly detection and intelligent fault diagnosis.

Dr. Kang Li received B.E. degree from Central South University in 2014 and Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2019. In 2021, he conducted postdoctoral research at Tsinghua University and served as an assistant researcher. He joined China University of Petroleum (Beijing) in 2023 and is currently a Lecturer and Master’s Supervisor at the College of Artificial Intelligence. His main research interests include oil and gas safety and fault diagnosis, as well as embodied intelligence. He has presided over several competitive research projects, including the National Natural Science Foundation of China (Young Scientists Fund, Category C), the National Postdoctoral Program for Innovative Talents (Category B), and multiple industry-sponsored projects. He has published more than 30 peer-reviewed papers as first or corresponding author in IEEE Transactions (including TNNLS, TII, TIM, TR) and other well-recognized SCI/EI journals and conferences, and holds three authorized Chinese invention patents as the first inventor. He serves as a committee member of the Artificial Intelligence and Robotics Education Committee of the Chinese Association of Automation, a committee member of the Youth Working Committee of the Chinese Association of Automation, a committee member of the Embodied Intelligence Committee of the Chinese Institute of Command and Control, and a committee member of the Unmanned Systems Committee of the Chinese Institute of Command and Control. His honors include the Best Paper Award at IC2ECS 2025, the Best Poster Paper Award at IEEE CYBER 2018, the Outstanding Case Award for “AI Empowering Industry Development” from the Beijing Association of Automation in 2025, and awards for excellence in supervising national-level student computer design competitions.