TY - CHAP
T1 - Nonlinear Quality-Related Fault Diagnosis
AU - Yin, Shen
AU - Wang, Guang
AU - Chen, Kaitao
AU - Wang, Anjie
N1 - Publisher Copyright:
© The Author(s) 2026.
PY - 2026
Y1 - 2026
N2 - Over the past decade, quality-related fault detection and diagnosis, as an important branch of multivariate statistical process monitoring, has become a critical component of industrial process monitoring. As one of the pioneering research teams in this field, we have achieved significant research outcomes. In particular, we have proposed many effective methods in nonlinear quality-related fault modeling, detection, isolation, and root cause analysis. In this chapter, we will provide a detailed summary and review of these methods and validate them through simulations using numerical examples and industrial case studies. We hope this will serve as a valuable reference for future research.
AB - Over the past decade, quality-related fault detection and diagnosis, as an important branch of multivariate statistical process monitoring, has become a critical component of industrial process monitoring. As one of the pioneering research teams in this field, we have achieved significant research outcomes. In particular, we have proposed many effective methods in nonlinear quality-related fault modeling, detection, isolation, and root cause analysis. In this chapter, we will provide a detailed summary and review of these methods and validate them through simulations using numerical examples and industrial case studies. We hope this will serve as a valuable reference for future research.
UR - https://www.scopus.com/pages/publications/105024538051
U2 - 10.1007/978-981-96-9033-6_17
DO - 10.1007/978-981-96-9033-6_17
M3 - 章节
AN - SCOPUS:105024538051
T3 - Lecture Notes in Control and Information Sciences
SP - 463
EP - 519
BT - Lecture Notes in Control and Information Sciences
PB - Springer Science and Business Media Deutschland GmbH
ER -