HKFSVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel FunctionSupport Vector Regression (HKFxe2x80x93SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multidomain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigenmatrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the betweenclass and withinclass scatters (SS) of the healthstage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKFxe2x80x93SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKFxe2x80x93SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Predicting the Remaining Useful Life of Ball Bearing Under Dynamic Loading Using Supervised Learning Rolling element bearing is one of the most critical components of rotating machinery. Its failure can be catastrophic and often results in both human and material losses. This paper presents a machine learning model to predict the wear process phenomena and remaining useful life of a bearing element using classification and regression techniques respectively. An algorithm is developed to recognize the underlying mapping function directly from the data using machine learning principles. Pearson correlation methodology is used to track the important features associated with the evolution of wear and understand its progression. Further, backward elimination technique with ordinary least squares regression results was used to track features for predicting the remaining useful life. The proposed approach is illustrated on a bearing failure data set from the national aeronautics and space agency. This study will be useful in forecasting the fault status of the bearing before it causes any major loss.