Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods

Name: Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods
Brand: Springer
SKU: 9783658167554
Price: 5450.0 INR
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by Zhiwen Chen

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ISBN13: 9783658167554
Binding: Paperback
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer Vieweg
Publication Date: January 9, 2017
Pages: 112
Original Price: EUR 74.99
Language: English
Edition: 2017
Item Weight: 180 grams
BISAC Subject(s): Automation, Applied, and Probability & Statistics / General

About The Book

From the Back Cover
Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.
Contents

A New Index for Performance Evaluation of FD Methods
CCA-based FD Method for the Monitoring of Stationary Processes
Projection-based FD Method for the Monitoring of Dynamic Processes
Benchmark Study and Real-Time Implementation

Target Groups

Researchers and students in the field of process control and statistical hypothesis testing
Research and development engineers in the process industry

About the AuthorZhiwen Chen's research interests include multivariate statistical process monitoring, model-based and data-driven fault diagnosis as well as their application to industrial processes. He is currently working at the School of Information Science and Engineering at Central South University, China.

About The Author

Zhiwen Chen's research interests include multivariate statistical process monitoring, model-based and data-driven fault diagnosis as well as their application to industrial processes. He is currently working at the School of Information Science and Engineering at Central South University, China.

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