论文部分内容阅读
A pre-filter combined with threshold self-learning wavelet algorithm is proposed for hydraulic pressure signals denoising. The denoising threshold is self-learnt in the steady flow state, and then modified under a given limit to make the mean square errors between reconstruction signals and desirable outputs minimum, so the corresponding optimal denoising threshold in a single operating case can be obtained. These optimal thresholds are used for the whole signal denoising and are different in various cases. Simulation results and comparative studies show that the present approach has an obvious effect of noise suppression and is superior to those of traditional wavelet algorithms and back-propagation neural networks. It also provides the precise data for the next step of pipeline leak detection using transient technique.
A pre-filter combined with threshold self-learning wavelet algorithm is proposed for hydraulic pressure signals denoising. The denoising threshold is self-learnt in the steady flow state, and then modified under a given limit to make the mean square errors between reconstruction signals and desirable output minimum, so the corresponding optimal denoising threshold in a single operating case can be obtained. These optimal thresholds are used for the whole signal denoising and are different in various cases. Simulation results and comparative studies show that the present approach has an obvious effect of noise suppression and is superior to those of traditional wavelet algorithms and back-propagation neural networks. It also provides the precise data for the next step of pipeline leak detection using transient technique.