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

Ying Shuang Zhang; Guo Qiang Wang; Ji Xin Wang; Li Juan Yang

doi:10.4028/www.scientific.net/AMR.317-319.2444

Paper Titles

An Improved Design of Stochastic Halftoning without Error Diffusion for Raster Image Processor
p.2427

Research on Stress Sensitivity of Low Permeability Sand Stone Reservoir
p.2432

Preliminary Study on Dissolved and Colloidal Organic Carbon in Tidal Creek of Chongming Dongtan
p.2436

Realization of QoS Control Strategies in the next Generation Network on the Basis of Differentiated Service Model and Priority of Inter-Frame
p.2440

Application of Wavelet-Based Fractal Dimension Threshold Denoising Method to Load Time History of Engineering Vehicle
p.2444

Study on Preparation and Factors of Amino Silicone with Low Viscosity
p.2449

Flexible Polishing Machine with Dual Grinding Heads for Aeroengine Blade and Blisk
p.2454

Robust Control with Input-Output Decoupling Technique for an Interior Permanent Magnet Synchronous Motor in Electrical Vehicle System
p.2461

Topology Optimization with a Penalty Factor in Optimality Criteria
p.2466

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319Application of Wavelet-Based Fractal Dimension...

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

Abstract:

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.