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Rolling Element Bearings are one of the most ubiquitous machine elements used in various machineries in the manufacturing industry. Prognosis and estimation of residual life of rolling element bearing are very important for efficient implementation of health monitoring and condition-based maintenance. In this paper, a rolling element bearing fault or degradation trend prediction is modeled using Gaussian Process Regression (GPR) method. Various vibration features based on signal complexity, namely Shannon entropy, permutation entropy, and approximate entropy are estimated to obtain the bearing degradation trend. When fault or degradation occurs in rolling element bearing, there is a subtle change in the dynamics of the system and subsequently, there are changes in the features extracted from the vibration signal. In this paper, a comparative analysis of various kernel functions of the GPR model is carried out using accuracy-based metrics. In addition, the combination of goodness of metric (monotonicity (Mon), robustness (Rob), and prognosability (Pro)), namely hybrid metric, is proposed to select the efficient bearing degradation trend of features. Further, the GPR at ARD exponential kernel has been employed to make the prognosis of degradation trend in bearings with a 95% confidence interval (CI). The proposed methodology is validated through a mathematical model of the simulated vibration signal. Finally, from the simulated and experimental data, it is demonstrated that the entropy features have better performance than the statistical features. • Entropy features (Shannon entropy, PE and ApEn) is proposed to obtain the degradation trend or fault in bearing. • A health indicator based on the hybrid metric is proposed for bearing degradation feature selection. • GPR technique has been used to prediction of bearing degradation trend with 95% CI. • The proposed method validated with simulations of the vibration signal. • Performance of GPR prediction efficiently estimated by accuracy based metrics. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel health indicator based on Dynamic Time Warping (DTW) is proposed for bearing degradation trend estimation. • Four degradation stages are recognized by using the CV and CV change rate. • Prediction of degradation trend is based on DTW-KRR and DTW-SVR. • Performance of DTW-KRR and DTW-SVR prediction are efficiently estimated by accuracy based metrics. An accurate and reliable forecast for fault/degradation trend has an enormous significance in predictive maintenance of industrial systems. Prognosis of bearing is essential for efficient implementation of condition based maintenance of rotating machinery to prevent sudden/unexpected breakdown. The Dynamic Time Warping (DTW) algorithm is proposed for the first time for bearing prognosis assessment and health trend prediction of rolling element bearing. The Dynamic Time Warping based value is then applied to obtain a Confidence Value (CV) to standardize the degradation process. The range of the parameter CV is zero to unity. The various degradation stages are recognized by using CV and the fault frequencies are obtained from the envelop spectrum. Further, the Kernel Ridge Regression (KRR) and Support Vector Regression (SVR) methodologies are applied on the DTW feature to forecast the bearing degradation trend. The effectiveness of DTW-KRR and DTW-SVR is analyzed by various accuracy metrics. The present study shows that the DTW and DTW-SVR are effective tools for bearing health degradation assessment and prognosis of rolling element bearing. [ABSTRACT FROM AUTHOR]

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]

Abstract: The steady state and dynamic characteristics including whirl instability of oil journal bearings with single axial groove located at the top of the bearing and then at some angular interval from the top from which oil is supplied at constant pressure are obtained theoretically. The Reynolds equation is solved numerically by finite difference method satisfying the appropriate boundary conditions. The dynamic behaviour in terms of stiffness and damping coefficients of fluid film and stability are found using a first-order perturbation method for each location of the groove. It has been shown that both load capacity, end flow is maximum when the feeding groove is at 12° location and thereafter the load capacity falls, stability improves for smaller groove angle and groove length at higher value of eccentricity ratio and speed. The stiffness and damping coefficient magnitude is found to be higher for the bearing with smaller groove angle and groove length, the difference between the hydrodynamic and hydrostatic load increases at 12° groove location. [Copyright &y& Elsevier]

• 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]