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.

Remaining Useful Life Estimation for Unknown Motors Using a Hybrid Modeling Approach Remaining useful life estimation is a research topic of high relevance in the area of structural mechanics. To predict the remaining useful lifetime of a motor, domain experts commonly employ physical simulations based on 3DCAD models. However, this process is laborious and in many cases no 3DCAD model is available. Also, setting up a simulation might require substantial efforts or might even be infeasible. This article focuses on the machine learning based estimation of the remaining useful life of unknown, derived motor types of an electric motor class based on simulations of known motor types, as well as data sheets and measurements. In particular, we propose the hybrid fusion method moSAIc that allows to transfer the knowledge inherent in physical degradation models of motors to unknown instances. Our experiments show that moSAIc outperforms other stateoftheart methods by a large margin in terms of both accuracy and robustness. Furthermore, compared to purely datadriven methods such as neural networks, moSAIc is explainable allowing domain experts to understand the reason for the predictions.