Data-driven design of fault diagnosis systems [electronic resource] : nonlinear multimode processes / Adel Haghani Abandan Sari.
2014
TA169.6
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Title
Data-driven design of fault diagnosis systems [electronic resource] : nonlinear multimode processes / Adel Haghani Abandan Sari.
ISBN
9783658058074 electronic book
3658058072 electronic book
9783658058067
3658058072 electronic book
9783658058067
Published
Wiesbaden : Springer Vieweg, 2014.
Language
English
Description
1 online resource (xix, 136 pages) : illustrations
Item Number
10.1007/978-3-658-05807-4 doi
Call Number
TA169.6
Dewey Decimal Classification
620/.00452
Summary
In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study effcient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target Groups Graduate students and scientists of automatic control and process engineering Engineers in field of process control and monitoring, mechatronic About the Author Adel Haghani Abandan Sari is research assistant with Institute of Automation, university of Rostock. His research interests include data-driven process monitoring and fault-tolerant control with focus on large-scale industrial processes.
Note
"PhD Thesis, University of Duisburg-Essen, 2013."
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed May 2, 2014).
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Table of Contents
Introduction
An overview of fault diagnosis techniques
Fault detection in multimode nonlinear systems
Fault detection in multimode nonlinear dynamic systems
Fault diagnosis in multimode nonlinear processes
Bayesian approach for fault treatment
Application and benchmark study
Summary.
An overview of fault diagnosis techniques
Fault detection in multimode nonlinear systems
Fault detection in multimode nonlinear dynamic systems
Fault diagnosis in multimode nonlinear processes
Bayesian approach for fault treatment
Application and benchmark study
Summary.