WAVELET DE-NOISING USING CUSTOMIZED THRESHOLDING FOR BEARING FAULT DETECTION
Keywords:
Denoising, Wavelet Analysis, Thresholding, Fault DetectionAbstract
Denoising is the process of recovering the original signal from the signal corrupted by noise. This problem of denoising has been topics of research.The method based on the wavelets have been the topic for research. In this paper, multiresolution analysis is applied to de-noise a simulated signal and signal obtained from a defective bearing. In this work, the characteristic features of vibration signals are extracted from noise Daubechies wavelets. Thresholding is one of the most commonly used processing tools in wavelet signal processing for noise removal. The methods used for estimating the threshold values are Rigrsure, Sqtwolog, Heusure and minimax. The two versions of thresholding a signal which are used to reduce the effect of noise are soft thresholding and hard thresholding. The propose technique called the customized thresholding function, is a linear combination of the soft and hard thresholding. Comparison of the new method with the existing thresholding methods is provided. Simulation results and the application on the actual signal demonstrate the advantage of using this method. Signal-to-noise ratio (SNR) and Mean Square Error (MSE) is used for comparing the use of wavelets and denoising techniques.
Downloads
References
Weaver J B, Yansun X, Healy D M Jr, and Cromwell L D (1991), “Filtering Noise from Images with Wavelet Transforms”, Magnetic Resonance in Medicine, Vol. 24, 288-295.
Donoho D L and Johnstone I M (1994), “Ideal Spatial Adaptation via Wavelet Shrinkage”, Biometrika, Vol. 81, 425- 455.
Donoho D L (1995), “De-noising by Soft-thresholding”, IEEE Trans. Info. Theory, Vol. 41, 613-627.
Carl and Taswell (2000), “The What, How and Why of Wavelet Shrinkage Denoising”, Computing in Science and Engineering, Vol. 2, 12-19
Prasad V V K D V, Siddaiah P and Rao P B (2008), “A New Wavelet Based Method for Denoising of Biological Signals”, IJCSNS International Journal of Computer Science and Network Security, Vol. 8(1), 238-244.
Lin J, Zuo M J, Fyfe K R (2004), “ Mechanical Fault Detection Based on the Wavelet De-noising Technique”, Journal of Vibration and Acoustics, Vol. 126, 9-16.
MATLAB Wavelets Toolbox 3.0 Release Notes, MATLAB Version 7.0 (R 14).
Burrus C S, Gopinath R A and Guo H (1998), “Introduction to Wavelets and Wavelet Transforms”, Prentice Hall.
Shi D F, Wang W J, and Qu L S (2004), “Defect Detection for Bearings using Envelope Spectra of Wavelet Transform”, Journal of Vibration and Acoustics, Vol. 126, 567-573.
Chang S G, Yu B and Vetterli M (2000), “Adaptive Wavelet Thresholding for Image Denoising and Compression”, IEEE Trans. Image Proc., Vol. 9, 1532-1546.
Patil M S, Jose Mathew, and Rajendrakumar P K (2008), “A Study on Wavelet Based Thresholding for Noise Reduction to Detect Bearing Defect”, National Journal of Technology, Vol. 4(3), 1-6.
