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  • Book
  • Open Access
  • © 2022

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Authors:

  1. Jing Wang

    1. Department of Automation, School of Electronical and Control Engineering, North China University of Technology, Beijing, China

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  2. Jinglin Zhou

    1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

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    You can also search for this author in PubMed   Google Scholar

  3. Xiaolu Chen

    1. College of Engineering, Peking University, Beijing, China

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  • Evaluates the practicality of data-driven methods in industrial process monitoring

  • Embeds manifold learning technology into multivariate statistical methods

  • Introduces partial least absolute technology to provide valuable guidance

  • This book is open access, which means that you have free and unlimited access.

Part of the book series: Intelligent Control and Learning Systems (ICLS, volume 3)

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Hardcover Book USD 59.99
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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xvii
    PDF

  2. Background

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 1-15Open Access

  3. Multivariate Statistics in Single Observation Space

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 17-30Open Access

  4. Multivariate Statistics Between Two-Observation Spaces

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 31-44Open Access

  5. Simulation Platform for Fault Diagnosis

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 45-58Open Access

  6. Soft-Transition Sub-PCA Monitoring of Batch Processes

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 59-77Open Access

  7. Statistics Decomposition and Monitoring in Original Variable Space

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 79-100Open Access

  8. Kernel Fisher Envelope Surface for Pattern Recognition

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 101-117Open Access

  9. Fault Identification Based on Local Feature Correlation

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 119-146Open Access

  10. Global Plus Local Projection to Latent Structures

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 147-172Open Access

  11. Locality-Preserving Partial Least Squares Regression

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 173-188Open Access

  12. Locally Linear Embedding Orthogonal Projection to Latent Structure

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 189-209Open Access

  13. New Robust Projection to Latent Structure

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 211-232Open Access

  14. Bayesian Causal Network for Discrete Variables

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 233-249Open Access

  15. Probabilistic Graphical Model for Continuous Variables

    • Jing Wang, Jinglin Zhou, Xiaolu Chen
    Pages 251-265Open Access
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About this book


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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Keywords

  • 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
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Authors and Affiliations

  • Department of Automation, School of Electronical and Control Engineering, North China University of Technology, Beijing, China

    Jing Wang

  • College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

    Jinglin Zhou

  • College of Engineering, Peking University, Beijing, China

    Xiaolu Chen

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About the authors

Jing Wang received the B.S. degree in Industry Automation and the Ph.D. degree in Control Theory and Control Engineering from the Northeastern University, in 1994 and 1998, respectively. She was a professor with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China, from 1999 to 2020, and a visiting professor at University of Delaware, USA, in 2014. Now she is a professor with School of Electrical and Control Engineering, North China University of Technology, Beijing, China. Her research interest is oriented to different aspects, including modeling, optimization, advance control, process monitoring, and fault diagnosis for complex industrial process; industrial artificial intelligence based on analysis and learning from big data.

Jinglin Zhou received the B.Eng., M.Sc., and Ph.D. degrees from Daqing Petroleum Institute, Hunan University, Changsha, China, and the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 1999, 2002, and 2005, respectively. He was Academic Visitor with the Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK. He is currently Professor with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing. His current research interests include stochastic distribution control, fault detection and diagnosis, variable structure control, and their applications.

Xiaolu Chen received the Ph.D. degree in Control Science and Engineering from Beijing University of Chemical Technology in 2021. She was a joint PhD student at the University of Duisburg Essen, Duisburg, Germany, from 2019 to 2020. Now she is a postdoctoral fellow at Peking University, Beijing, China.  Her major is  control science and engineering.  Her research interests include modelling and fault diagnosis of complex industrial processes, data causality analysis, and intelligent learning algorithms.
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Bibliographic Information

  • Book Title: Data-Driven Fault Detection and Reasoning for Industrial Monitoring

  • Authors: Jing Wang, Jinglin Zhou, Xiaolu Chen

  • Series Title: Intelligent Control and Learning Systems

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

  • Publisher: Springer Singapore

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022

  • License: CC BY

  • Hardcover ISBN: 978-981-16-8043-4Published: 04 January 2022

  • Softcover ISBN: 978-981-16-8046-5Published: 21 January 2023

  • eBook ISBN: 978-981-16-8044-1Published: 03 January 2022

  • Series ISSN: 2662-5458

  • Series E-ISSN: 2662-5466

  • Edition Number: 1

  • Number of Pages: XVII, 264

  • Number of Illustrations: 19 b/w illustrations, 115 illustrations in colour

  • Topics: Industrial Automation, Computational Intelligence, Automation

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Hardcover Book USD 59.99
Price excludes VAT (USA)
Learn about institutional subscriptions