Special Issue Article Open Access

Noise Reduction of Vibration Signals in Rotary Machines using Neighbourhood Correlation of Wavelet Transform Coefficients

Abstract

Faulty gears and bearings are a major source of problem in rotating machines. These faults appear as impulses at periodic intervals resulting in form of characteristic frequency of the machinery. To retrieve the characteristic fault frequencies of the vibration signal, denoising of signal is an essential preprocessing step. This paper presents two robust techniques for denoising of vibration signals corrupted by Additive White Gaussian Noise. The proposed method uses the Stationary Wavelet Transform (SWT) and the Dual Tree Complex Wavelet Transform (DTCWT). The proposed methods are very efficient due to their shift invariance property and have reduced aliasing effect than other methods such as Discrete Wavelet Transform(DWT), Continuous Wavelet Transform(CWT), Short Time Fourier Transform(STFT) etc. For reduction of noise, owing to the fact that neighbouring wavelet coefficients belonging to signal are correlated, the thresholding of noisy wavelet coefficients is done by selecting a window and comparing the averaged noisy wavelet coefficients inside the window with a threshold. Denoising results are compared by varying the window size and the best window length is chosen for denoising. Experiments are carried out on simulated vibration signals as well as on real time signals. The comparison of the results of the proposed methods using DTCWT and SWT for synthetic signal is done using the signal to noise ratio(SNR). For comparison of results of the real time signal, another statistical parameter Kurtosis is chosen.

Shweta Garde, Preety D Swami

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