Adaptive Remaining Useful Lifetime Prediction of Magnetic Head under Varying Stress Conditions Current Remaining useful life studies mainly focus on the linear degradation case. A few nonlinear degradation models often rely on parameters learning process from a batch of samples obtained from the same population. This result in large estimation biases and uncertainties in term of RUL prediction under varying stress conditions. To address this problem, this paper proposed an adaptive RUL prediction method only using the observed head wear data. First, an exponential item is incorporated into a power law wear model to build a nonlinear wear model under varying stress conditions. Second, PF method was used to update the model parameters dynamically. Third, an adaptive strategy was developed based on expectationmaximization algorithm to update these initial values of parameters of degradation model recursively. Finally, a reallife data set was used to verify the effectiveness of our proposed approach, and the results show that the proposed approach can improve the prediction accuracy significantly.

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.