Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

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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 proce...

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Bibliographic Details
Main Author: Wang, Jing (auth)
Other Authors: Zhou, Jinglin (auth), Chen, Xiaolu (auth)
Format: Electronic Book Chapter
Language:English
Published: Springer Nature 2022
Series:Intelligent Control and Learning Systems
Subjects:
Robotics
Artificial intelligence
Multivariate causality analysis
Process monitoring
Manifold learning
Fault diagnosis
Data modeling
Fault classification
Fault reasoning
Causal network
Probabilistic graphical model
Data-driven methods
Industrial monitoring
Open Access
Online Access:DOAB: download the publication
DOAB: description of the publication
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