The 19th International Conference on Industrial Engineering and Engineering Management pp 279-288 | Cite as
Fault Diagnosis of Wet Flue Gas Desulphurization System Based on KPCA
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 sensorsNotes
Acknowledgment
This paper is done by the careful guidance of professor Zhang. In the process of the paper, tutor and senior sister pay for great effort. I thank my tutor and senior sister great help and deep respect.
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