A novel signal denoising method using stationary wavelet transform and particle swarm optimization with application to rolling element bearing fault diagnosis

In this paper, a novel signal denoising method based on Stationary Wavelet Transform and Particle Swarm Optimization Algorithm (SWT-PSO) is proposed. The threshold values for wavelet denoising are selected using the meta-heuristic algorithm of particle swarm optimization such that the denoised signal has maximum weighted kurtosis. In many faulty rotating machinery elements such as rolling element bearings and gears, impact-type vibration signals are produced. Such impact type vibration signals have a high value of kurtosis. The proposed SWT-PSO scheme can be used for the enhancement of fault signature in rotating machinery by increasing the kurtosis of such signals. The proposed algorithm has been applied on simulated as well as experimental defective rolling element bearing signal. Vibration signals from rolling element bearing with inner race and outer race defects are acquired by accelerometers on a Machinery Fault Simulator (MFS) setup. Then the proposed methodology is applied successfully to denoise the signals at various noise levels. Further, Spectrum envelope of the denoised vibration signal is used to obtain the characteristic defect frequencies. The developed methodology is finally compared with widely used Minimum Entropy Deconvolution (MED) algorithm. It has been demonstrated that the proposed algorithm has better capability of extracting the impulsive characteristic as well as preserving the signal geometrical structure than compared to MED.