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Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Jing Wang, Jinglin Zhou, Xiaolu Chen
2022
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This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.
This book is included in DOAB.
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Keywords
- artificial intelligence
- Automatic control engineering
- Causal network
- Computer science
- Computing & information technology
- Data Modeling
- data-driven methods
- Electronics & communications engineering
- Electronics engineering
- fault classification
- fault diagnosis
- Fault reasoning
- Industrial monitoring
- manifold learning
- Multivariate causality analysis
- open access
- Probabilistic graphical model
- process monitoring
- robotics
- Technology, engineering, agriculture
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
Links
DOI: 10.1007/978-981-16-8044-1Editions