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- W2511211188 abstract "Principal component analysis (PCA) is a common fault detection method. But it is difficult to get high accuracy· if it is applied to complex nonlinear system. Faced with complex system such as chiller· this paper proposes using kernel principal component analysis (KPCA) for fault detection. But· the selection of kernel parameters is a problem in the implement of KPCA algorithm. Genetic algorithm (GA) is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection rate. This method is verified by ASHRAE 1043-RP data. The results show that it is better than PCA. And it can improve the accuracy of fault detection." @default.
- W2511211188 created "2016-09-16" @default.
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- W2511211188 date "2016-05-01" @default.
- W2511211188 modified "2023-09-28" @default.
- W2511211188 title "Fault detection of chiller based on improved KPCA" @default.
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- W2511211188 doi "https://doi.org/10.1109/ccdc.2016.7531487" @default.
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