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Licensed Unlicensed Requires Authentication Published by De Gruyter January 7, 2019

A review of data-driven fault detection and diagnosis methods: applications in chemical process systems

  • Norazwan Md Nor

    Norazwan Md Nor received both his BSc and MSc degrees in Chemical Engineering from Universiti Sains Malaysia (USM), Malaysia, in 2009 and 2012, respectively. His research interests include fault detection and diagnosis of chemical process systems and application of artificial intelligence in fault detection and diagnosis.

    , Che Rosmani Che Hassan

    Che Rosmani Che Hassan received her BEng in Chemical Engineering from Universiti Teknologi Malaysia (UTM), and her MSc and PhD degrees from the University of Sheffield, UK. She is an associate professor in the Department of Chemical Engineering, University of Malaya, Malaysia. Her research interests include environmental sciences, issues and assessment, qualitative and quantitative risk assessment, process safety, and hazardous waste.

    and Mohd Azlan Hussain

    Mohd Azlan Hussain is a professor in the Department of Chemical Engineering, Faculty of Engineering, University of Malaya, Malaysia. He received his BSc and MSc from the University of Sheffield, UK, and University of Tulsa, USA, respectively, and his PhD from Imperial College of Technology, University of London. His area of expertise is advanced and nonlinear process controls, modeling, and chemical process simulation. He has published more than 240 journal and conference papers and has more than 30 years of experience on various levels of professional chemical engineer organizations.

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Abstract

Fault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.

About the authors

Norazwan Md Nor

Norazwan Md Nor received both his BSc and MSc degrees in Chemical Engineering from Universiti Sains Malaysia (USM), Malaysia, in 2009 and 2012, respectively. His research interests include fault detection and diagnosis of chemical process systems and application of artificial intelligence in fault detection and diagnosis.

Che Rosmani Che Hassan

Che Rosmani Che Hassan received her BEng in Chemical Engineering from Universiti Teknologi Malaysia (UTM), and her MSc and PhD degrees from the University of Sheffield, UK. She is an associate professor in the Department of Chemical Engineering, University of Malaya, Malaysia. Her research interests include environmental sciences, issues and assessment, qualitative and quantitative risk assessment, process safety, and hazardous waste.

Mohd Azlan Hussain

Mohd Azlan Hussain is a professor in the Department of Chemical Engineering, Faculty of Engineering, University of Malaya, Malaysia. He received his BSc and MSc from the University of Sheffield, UK, and University of Tulsa, USA, respectively, and his PhD from Imperial College of Technology, University of London. His area of expertise is advanced and nonlinear process controls, modeling, and chemical process simulation. He has published more than 240 journal and conference papers and has more than 30 years of experience on various levels of professional chemical engineer organizations.

Acknowledgments

The authors are grateful to the Universiti Sains Malaysia (USM) for providing the ASTS scholarship, and the University of Malaya (UM) and the Ministry of Higher Education Malaysia (MOHE) for supporting this work through the FRGS grant FP064-2015A.

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Received: 2017-08-02
Accepted: 2018-08-20
Published Online: 2019-01-07
Published in Print: 2020-05-26

©2020 Walter de Gruyter GmbH, Berlin/Boston

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