Fault Diagnosis of Wet Flue Gas Desulphurization System Based on KPCA

Yu-ping Zheng () and Li-ping Zhang
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Yu-ping Zheng: Fuzhou University
Li-ping Zhang: Fuzhou University

Chapter Chapter 27 in The 19th International Conference on Industrial Engineering and Engineering Management, 2013, pp 279-288 from Springer

Abstract: Abstract Fault detection and diagnosis for sensor are necessary, which affect the performance of the thermal power plant of wet flue gas desulphurization system seriously. A fault diagnosis method using kernel principal component analysis (KPCA) is proposed to affectively capture 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 faults are detected by calculating the statistics of the square prediction error (SPE) and identified by calculating the change diagram of contribution percentage of Hostelling $$ {T^2} $$ . At last, employing the actual data from wet flue gas desulphurization system of Huaneng Fuzhou power plant, it’s proved effectively to detect and identify four kinds of faults, which is the complete invalidation fault, fixed bias fault, drift bias fault and precision degradation fault. The result shows the KPCA method has a good performance in fault detection and diagnosis.

Keywords: Fault detect and diagnosis; Gas desulphurization; KPCA; Wet flue sensors (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-37270-4_27

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DOI: 10.1007/978-3-642-37270-4_27

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