Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different...

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Published inProcesses Vol. 12; no. 2; p. 251
Main Authors Melo, Afrânio, Câmara, Maurício Melo, Pinto, José Carlos
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2024
Subjects
Algorithms
Artificial intelligence
Artificial neural networks
Chemical industry
Control charts
Data processing
Didacticism
Fault diagnosis
Fisher, Ronald Aylmer
Herbicides
Machine learning
Methods
Monitoring
Multivariate analysis
Neural networks
Pesticides industry
Principal components analysis
Production processes
Software
Software development
Statistical analysis
Statistics
Surveys
Variables
Brazil
United Kingdom
United States
Tennessee
Germany
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Abstract This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.
AbstractList This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.
Audience Academic
Author Câmara, Maurício Melo
Pinto, José Carlos
Melo, Afrânio
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  fullname: Pinto, José Carlos
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RelatedPersons Fisher, Ronald Aylmer
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SubjectTerms Algorithms
Artificial intelligence
Artificial neural networks
Chemical industry
Control charts
Data processing
Didacticism
Fault diagnosis
Fisher, Ronald Aylmer
Herbicides
Machine learning
Methods
Monitoring
Multivariate analysis
Neural networks
Pesticides industry
Principal components analysis
Production processes
Software
Software development
Statistical analysis
Statistics
Surveys
Variables
Title Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
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