Realtime monitoring for multivariate statistical process with online multiscale filtering
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Abstract:
By analyzing shortages of current MSPCA model, an online multivariable statistical process monitoring method is proposed, which uses some concepts from online multiscale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixedlength data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an online multiscale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multiscale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.
