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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 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.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Nature., 2022

Collation

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Language

English

ISBN/ISSN

9789811680441

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Data Modeling
Multivariate causality analysis
Process monitoring
Manifold learning
Fault diagnosis
Fault classification
Fault reasoning
Causal network

Specific Detail Info

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Statement of Responsibility

Wang, Jing Zhou, Jinglin Chen, Xiaolu

Other Information

Cataloger

agus

Source

https://link.springer.com/book/10.1007/978-981-16-8044-1

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-981-16-8044-1

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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