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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Vydané v:Applied Mechanics and Materials Ročník 623; číslo Engineering Research and Designing for Industry; s. 202 - 210
Hlavní autori: Wang, Qiu Yan, Xu, Ping, Wang, Kai, Wang, You Cai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Zurich Trans Tech Publications Ltd 01.08.2014
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ISBN:9783038352228, 3038352225
ISSN:1660-9336, 1662-7482, 1662-7482
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Abstract 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.
AbstractList 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.
Author Wang, Qiu Yan
Wang, Kai
Xu, Ping
Wang, You Cai
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  surname: Wang
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  email: wangyoycai@126.com
  organization: Xi’an Research Institute of Hi-tec
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10.1016/j.compchemeng.2004.02.036
10.1016/j.ces.2003.09.012
10.1162/089976698300017467
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Issue Engineering Research and Designing for Industry
Keywords Kernel Principal Component Analysis (KPCA)
Fault Detection
Diagnosis
Complex Control System
Sensor
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SubjectTerms Control systems
Fault detection
Fault diagnosis
Faults
Kernels
Nonlinearity
Sensors
Statistics
Title Fault Detection and Diagnosis for Sensor in Complex Control System Based on KPCA
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