Fault Detection and Diagnosis for Sensor in Complex Control System Based on KPCA

The Fault detection and diagnosis for sensors are important for the performance of the complex control system seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional fea...

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Bibliographic Details
Published in:Applied Mechanics and Materials Vol. 623; no. Engineering Research and Designing for Industry; pp. 202 - 210
Main Authors: Wang, Qiu Yan, Xu, Ping, Wang, Kai, Wang, You Cai
Format: Journal Article
Language:English
Published: Zurich Trans Tech Publications Ltd 01.08.2014
Subjects:
Control systems
Fault detection
Fault diagnosis
Faults
Kernels
Nonlinearity
Sensors
Statistics
Kernel Principal Component Analysis (KPCA)
Fault Detection
Diagnosis
Complex Control System
Sensor
ISBN:9783038352228, 3038352225
ISSN:1660-9336, 1662-7482, 1662-7482
Online Access:Get full text
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Summary:The Fault detection and diagnosis for sensors are important for the performance of the complex control system seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method is used in diagnosing for four common sensor faults. At first its fault is detected by Q statistic; secondly its fault is identified by T2 contribution percent change. The simulation and the practical result show the KPCA method has good performance on complex control system in sensor fault detection and diagnosis.
Bibliography:Selected, peer reviewed papers from the 2013 International Conference on Mechatronics and Materials Engineering (ICMME 2013), May 25-27, 2013, Qiqihar, China
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ISBN:9783038352228
3038352225
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.623.202