Investigation on the effects of measurement and temporal uncertainties on rolling element bearings prognostics

Abstract

Estimation of remaining useful life (RUL) of rolling element bearings (REBs) has a major effect on improving the reliability in the industrial plants. However, due to the complex nature of the fault propagation in these components, their prognosis is affected by various uncertainties. This effect is intensified when the recorded data is offline, which is very common for many industrial machines due to the lower cost rather than the online monitoring strategy. In the present paper, in order to overcome the shortcoming of the feed-forward neural network (FFNN) in REBs prognostics, a new method for considering two main uncertainties (caused by the measurement and process noises) is proposed, in the presence of offline data acquisition. Inthe proposed method, the primary RUL probability distribution corresponded to each offline measured data is predicted, utilizing the outputs of trained FFNNs. Then, the predicted RUL distribution will become more robust in confronting the temporal changes, by taking into account the approval of pervious stage predictions to the present prediction. As a result, the overall probability distribution of REBs RUL and also its confidence levels (CLs) areobtained. Finally, the evaluation of the proposed method is performed byutilizing bearing experimental datasets. The results show that the proposed method has the capability to express the estimated RUL CLs in the offline data acquisition method, effectively. By providing a probabilistic perspective, the proposed method can improve the reliability of the asset and also the decision-making about the future of the industrial plants.