Spatio-Temporal Modeling and Meta-Learning for Industrial Monitoring From Unsupervised Anomaly Detection to Few-Shot Fault Diagnosis

Spatio-Temporal Modeling and Meta-Learning for Industrial Monitoring From Unsupervised Anomaly Detection to Few-Shot Fault Diagnosis

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Engineering Applications of Computational Methods

Spatio-Temporal Modeling and Meta-Learning for Industrial Monitoring

From Unsupervised Anomaly Detection to Few-Shot Fault Diagnosis

Kang Li

Technology & Engineering / Industrial Engineering

This book presents data-driven methods for anomaly detection and 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, long-term temporal dependencies, and frequently changing operating conditions. 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 anomaly detection and intelligent fault diagnosis under small-sample and cross-domain settings. The methods are developed with industrial scenarios in mind and aim to support reliable industrial process health monitoring.

Dr. Kang Li received his B.E. degree in Automation from Central South University, Changsha, China, in 2014, and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China, in 2019, where he was admitted to the Ph.D. program directly and supervised by Prof. Min Tan, a National Science Fund for Distinguished Young Scholars recipient. From July 2019 to February 2021, he worked as an Engineer at the Information Science Research Institute of China Electronics Technology Group Corporation, where he was involved in project planning and feasibility studies. From April 2021 to June 2023, he was a Postdoctoral Research Fellow and Assistant Researcher with the Department of Automation, Tsinghua University, focusing on fault diagnosis and fault-tolerant control of complex systems. Since October 2023, he has been conducting his second postdoctoral research under the supervision of Prof. Laibin Zhang, Academician of the Chinese Academy of Engineering, at China University of Petroleum (Beijing). He is currently a Lecturer and Master’s Supervisor with the College of Artificial Intelligence, China University of Petroleum (Beijing). His research interests include oil and gas safety and fault diagnosis, robotics with perception and interaction, and embodied intelligence. He has presided over several competitive research projects, including the National Natural Science Foundation of China (Young Scientists Fund, Category C) and the National Postdoctoral Program for Innovative Talents (Category B), as well as multiple industry-sponsored projects. He has published more than 20 peer-reviewed papers as first or corresponding author in IEEE Transactions (including IEEE TNNLS, TII, TIM, and TR) and other well-recognized SCI/EI journals and conferences, and holds three authorized Chinese invention patents as the first inventor. Dr. Li serves as a committee member of the Artificial Intelligence and Robotics Education Committee of the Chinese Association of Automation, and the Embodied Intelligence and Unmanned Systems Committees of the Chinese Society of Command and Control. His honors include the Best Paper Award at IEEE IC2ECS 2025, the Best Poster Paper Award at IEEE CYBER 2018, and awards for excellence in supervising national-level student competitions.


Publication Date: 26 September 2026
Publisher: Springer Nature Singapore
Imprint: Springer
ISBN-13: 9789819238255
Format: Hardback

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