Abstract
For complicated nonlinear systems, the data inevitably have noise, random disturbance, Traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix K for fault detection with large sample sets. So an improved KPCA method based on wavelet denoising is proposed. First, wavelet denoising method is used for data processing, then the improved KPCA method can reduce calculational complexity of fault detection. The proposed method is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection.
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