Visual Analytics for Process Monitoring
From Time Series to Interpretable Visual Representations
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Beschreibung
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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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.
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
Autor*in
Ibrahim Yousef
Themen in »Visual Analytics for Process Monitoring«
Visual analytics Data-driven fault detection and diagnosis Time-series imaging Process monitoring Interpretable machine learning Industrial process analytics Time series representation learning Process systems engineering Engineering applications of artificial intelligence