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Independent signal is stricter than the non-correlated signal in math. Independent component analysis (ICA) can extract independent signals, so it is better than principal component analysis (PCA) when they are used to diagnose faults. However ICA isn't suited for no-obvious faults which are caused by inputs' small changes. In order to solve this problem, multi-scale ICA (MSICA) is investigated in...
Although Kernel Principle Component Analysis(KPCA)has been used to monitoring nonlinear processes, it is not well suited for fault diagnosis. In order to solve this problem, a new method of fault detection and diagnosis for nonlinear processes based on KPCA and Least Squares Support Vector Machine(LSSVM) is proposed. The KPCA is used to monitor faults and extract feature and LSSVM model is used to...
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