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

✍ Scribed by Zhiwen Chen (auth.)

Publisher: Springer Vieweg
Year: 2017
Tongue: English
Leaves: 124
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XIX
Introduction....Pages 1-11
The Basics of Fault Detection....Pages 13-30
Evaluation and Comparison of T 2 and Q Statistics for Fault Detection....Pages 31-42
Canonical Correlation Analysis-based Fault Detection Methods....Pages 43-58
Improved CCA-based Fault Detection Methods....Pages 59-69
A Projection-based FD method for Dynamic Processes with deterministic disturbances....Pages 71-78
Benchmark Studies....Pages 79-97
Conclusions and Future Work....Pages 99-101
Back Matter....Pages 103-112

✦ Subjects

Control;Appl.Mathematics/Computational Methods of Engineering

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