Visual Analytics for Process Monitoring: From Time Series to Interpretable Visual Representations - Rilegato

Yousef, Ibrahim; Shah, Sirish L.; Gopaluni, R. Bhushan

Rilegato

ISBN 10: 3032221250 ISBN 13: 9783032221254

Casa editrice: Springer-Verlag GmbH, 2026

Riassunto

In the era of big data, process industries face the challenge of analyzing massive and complex data to extract information for effective process monitoring. This book introduces a novel paradigm called visual analytics. Visual analytics transforms chronological process data into visual formats to uncover patterns. This paradigm allows process experts to relate visual patterns to operational conditions and, consequently, support informed decision-making.

The book explores three pathways within the visual analytics paradigm: (i) feature engineering, in which predefined mappings are used to convert time series data into visual representations; (ii) architecture engineering, which develops neural network architectures to directly learn visual representations; and (iii) data engineering, which employs contrastive learning to highlight differences and similarities in the data without relying on annotations.

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Informazioni sull'autore

Ibrahim Yousef received his PhD in Chemical and Biological Engineering from the University of British Columbia, Canada. His research focuses on industrial process monitoring and fault detection, with an emphasis on data-driven and visual analytics approaches for time-series analysis. He holds a bachelor’s degree in chemical engineering from the University of Abu Dhabi. His academic and research interests lie at the intersection of process systems engineering, data analytics, and machine learning for industrial applications.

 Dr. Sirish L. Shah, PhD, FCAE, FCIC, FIEEE is Emeritus Professor with the Department of Chemical and Materials Engineering at the University of Alberta, where he held the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 2000 to 2012. He was on faculty at the University of Alberta from 1978 to 2016. Shah has held visiting appointments at Oxford University and Balliol College as a SERC fellow, Kumamoto University (Japan) as a Senior Research Fellow of the Japan Society for the Promotion of Science, among other appointments.

The main areas of Shah’s current research are process and performance monitoring, system identification and design, analysis and rationalization of alarm systems. Results from Shah’s research group have been translated into commercial software for process and performance monitoring and advanced alarm tools. He has consulted widely with the process industry and control software vendors.

Dr. R. Bhushan Gopaluni leads research activities at the UBC DAIS Lab. He is a Professor in the Department of Chemical and Biological Engineering and Vice-Provost and Associate Vice-President, Faculty Planning in the Office of the Provost and Vice-President Academic, UBC Vancouver. From 2017 to 2022, he was the Associate Dean for Education and Professional Development in the UBC Faculty of Applied Science. He received a Ph.D. from the University of Alberta in 2003 and a Bachelor of Technology from the Indian Institute of Technology, Madras in 1997 both in the field of chemical engineering.

He is one of the leading experts on data analytics for the processing industry and has authored over 110 refereed articles in reputed international Journals and conferences. His publications have been recognized through best paper awards and keynote presentations.

Dalla quarta di copertina

In the era of big data, process industries face the challenge of analyzing massive and complex data to extract information for effective process monitoring. This book introduces a novel paradigm called visual analytics. Visual analytics transforms chronological process data into visual formats to uncover patterns. This paradigm allows process experts to relate visual patterns to operational conditions and, consequently, support informed decision-making.

The book explores three pathways within the visual analytics paradigm: (i) feature engineering, in which predefined mappings are used to convert time series data into visual representations; (ii) architecture engineering, which develops neural network architectures to directly learn visual representations; and (iii) data engineering, which employs contrastive learning to highlight differences and similarities in the data without relying on annotations.

In addition, this book:

• Enables the qualitative interpretation of process behavior through image-based representations of time-series data
• Organized systematically into three main parts: feature engineering, architecture engineering, and data engineering
• Presents the content in a concise and accessible manner, suitable for researchers, graduate students, and practitioners

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Dettagli dell'edizione

• Editore: Springer-Verlag GmbH
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 3032221250
• ISBN 13: 9783032221254
• Rilegatura: Copertina rigida
• Numero di pagine: 92
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania