Search

In this paper, a modified nonlocal means denoising (NL-means) algorithm is proposed for rolling element bearing fault diagnosis. Although, nonlocal means denoising is widely used in image processing, this algorithm is rarely used in 1-D signal processing. The present work deals with application of 1-D nonlocal means denoising method for enhancement of fault signature in rolling element bearings. The parameters for the NL-means method are obtained by maximizing kurtosis value of bearing vibration signal. The proposed method is compared with minimum entropy deconvolution (MED) technique and the results indicate that the proposed method performs better for bearing fault diagnosis. The method is shown to be robust against various noise levels. Further, envelope spectrum of bearing vibration signal is also used to obtain characteristic bearing defect frequencies. [ABSTRACT FROM AUTHOR]

• Propose a novel health indicator based on Kullback-Leibler divergence (KLD) method. • KLD value converted into the confidence value in the range of 0–1. • Estimation of residual life of bearing using the Gaussian Process Regression model. • The proposed method shows excellent performance on the experimental data set. Accurate monitoring of degradation in bearing is essential for preventing unexpected shutdown of a machinery system. This paper proposes a novel health degradation indicator for machineries, based on a Kullback-Leibler divergence. This would help assess degradation in rolling element bearings and make prediction on the remaining useful life. The confidence value is proposed as the bearing health degradation indicator for prognostics approach or for assessing the bearings' degradation trend. It is able to quantify different degradation stages (healthy, severe and failure stages) of rolling element bearings. Further, Gaussian Process Regression has been used to make the prognosis of degradation trend in bearings with a 95% confidence interval, remove outliers from confidence value, and estimate the remaining useful life of bearing. Experimental data on the test bearings were studied to demonstrate the proposed method's capabilities. It can assess an accurate degradation trend and health state of bearing. [ABSTRACT FROM AUTHOR]