Application of Wavelet-Based Fractal Dimension Threshold Denoising Method to Load Time History of Engineering Vehicle

The load time history signal of engineering vehicle is usually polluted by various nonstationary and stationary noises in the field test. An approach based on wavelet transform (WT) and fractal dimension (FD) is proposed in order to improve the adaptability and efficiency of denoising. This method initially decomposes the original signal into detail and approximation space in the WT domain by WT-based multiresolution decomposition. The short-time fractal dimension of detail coefficient is calculated at each scale. After the application of the binary processing to the short-time fractal dimensions, the locations where the thresholding of the detail coefficients has to be executed are ensured. The desired load signal is provided by applying WT-based multiresolution reconstruction to the processed detail coefficients and the unprocessed approximation coefficients. The proposed method is applied to an actual load time history signal of engineering vehicle. And the performance of this method is compared with that of the WT-based hard thresholding denoising method. The results show that this method is an alternative way to process the load time history signal of engineering vehicle.