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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Superior document:Intelligent Control and Learning Systems ; v.3
:
Wang, Jing, 1974 April 21-
TeilnehmendeR:
Zhou, Jinglin.
Chen, Xiaolu.
Place / Publishing House:Singapore : : Springer Singapore Pte. Limited,, 2022.
©2022.
Year of Publication:2022
Language:English
Series:Intelligent Control and Learning Systems
Physical Description:1 online resource (277 pages)
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(OCoLC)1292353116
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(PPN)262175452
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record_format marc
spelling Wang, Jing, 1974 April 21-
Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
Springer Nature 2022
Singapore : Springer Singapore Pte. Limited, 2022.
©2022.
1 online resource (277 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Intelligent Control and Learning Systems ; v.3
Description based on publisher supplied metadata and other sources.
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.
English
Robotics bicssc
Artificial intelligence bicssc
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
981-16-8043-4
Zhou, Jinglin.
Chen, Xiaolu.
Intelligent Control and Learning Systems
language English
format eBook
author Wang, Jing, 1974 April 21-
spellingShingle Wang, Jing, 1974 April 21-
Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
Intelligent Control and Learning Systems ;
author_facet Wang, Jing, 1974 April 21-
Zhou, Jinglin.
Chen, Xiaolu.
author_variant j w jw
author2 Zhou, Jinglin.
Chen, Xiaolu.
author2_variant j z jz
x c xc
author2_role TeilnehmendeR
TeilnehmendeR
author_sort Wang, Jing, 1974 April 21-
title Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
title_full Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
title_fullStr Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
title_full_unstemmed Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
title_auth Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
title_new Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
title_sort data-driven fault detection and reasoning for industrial monitoring.
series Intelligent Control and Learning Systems ;
series2 Intelligent Control and Learning Systems ;
publisher Springer Nature
Springer Singapore Pte. Limited,
publishDate 2022
physical 1 online resource (277 pages)
isbn 981-16-8044-2
981-16-8043-4
callnumber-first T - Technology
callnumber-subject T - General Technology
callnumber-label T59
callnumber-sort T 259.5
illustrated Not Illustrated
oclc_num 1292353116
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hierarchy_parent_title Intelligent Control and Learning Systems ; v.3
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