Spatio-Temporal Modeling and Meta-Learning for Industrial Monitoring
From Unsupervised Anomaly Detection to Few-Shot Fault Diagnosis
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Beschreibung
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.
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.
Develops graph-based spatio-temporal modeling strategies that learn sparse variable interactions and temporal dynamics Validates proposed methods through real industrial monitoring scenarios Presents integrated framework linking spatio-temporal anomaly detection and meta-learning-based few-shot fault diagnosis
Autor*in
Kang Li
Themen in »Spatio-Temporal Modeling and Meta-Learning for Industrial Monitoring«
Complex industrial processes anomaly detection Data-driven Unsupervised anomaly detection Deep learning Few shot fault diagnosis fault diagnosis