Fault detection of feed water treatment process using PCA-WD with parameter optimization. (May 2017) - Zhang, Shirong
Abstract: Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000 MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Abstract : Highlights: Wavelet denoise (WD) is combined with PCA to form a new algorithm (PCA-WD) for fault detection. Parameter selection of PCA-WD is formulated as an optimization problem where PSO is employed for optimization solution. PCA-WD is validated using a feed water treatment processes in coal-fired power plants consisting of two 1000 MW generation units. PCA-WD is able to detect four kinds of conditions, as bias fault, drift fault, broken line fault and normal condition, more precisely than traditional PCAs.