Data-Driven Fault Detection and Reasoning for Industrial Monitoring
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...
| Format: | eBook |
|---|---|
| Language: | English |
| Published: |
Springer Nature
2022
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| Series: | Intelligent Control and Learning Systems
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| Subjects: | |
| Online Access: | Open Access: DOAB: description of the publication Open Access: DOAB, download the publication |
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| 020 | |a 978-981-16-8044-1 | ||
| 020 | |a 9789811680441 | ||
| 024 | 7 | |a 10.1007/978-981-16-8044-1 |2 doi | |
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| 720 | 1 | |a Wang, Jing |4 aut | |
| 720 | 1 | |a Chen, Xiaolu |4 aut | |
| 720 | 1 | |a Zhou, Jinglin |4 aut | |
| 245 | 0 | 0 | |a Data-Driven Fault Detection and Reasoning for Industrial Monitoring |
| 260 | |b Springer Nature |c 2022 | ||
| 300 | |a 1 online resource (264 p.) | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Intelligent Control and Learning Systems | |
| 506 | 0 | |a Open Access |f Unrestricted online access |2 star | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f by/4.0/ |2 cc |u http://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Artificial intelligence |2 bicssc | |
| 650 | 7 | |a Robotics |2 bicssc | |
| 653 | |a Causal network | ||
| 653 | |a Data modeling | ||
| 653 | |a Data-driven methods | ||
| 653 | |a Fault classification | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Fault reasoning | ||
| 653 | |a Industrial monitoring | ||
| 653 | |a Manifold learning | ||
| 653 | |a Multivariate causality analysis | ||
| 653 | |a Open Access | ||
| 653 | |a Probabilistic graphical model | ||
| 653 | |a Process monitoring | ||
| 793 | 0 | |a DOAB Library. | |
| 856 | 4 | 0 | |u https://directory.doabooks.org/handle/20.500.12854/77320 |7 0 |z Open Access: DOAB: description of the publication |
| 856 | 4 | 0 | |u https://library.oapen.org/bitstream/20.500.12657/52452/1/978-981-16-8044-1.pdf |7 0 |z Open Access: DOAB, download the publication |