Prognostics of rolling element bearings using shock pulse method and vibration method records and employing feedforward neural-network

Early fault detection of the rolling element bearings has a very important role in increasing the reliability of rotating machines.It leads to better decision-making for maintenance activities.  In recent decades, the shock pulse method has been developed to detect faults in the early stage of rolling element bearings degradation. In this paper, the accuracy of the remaining useful life estimation using extracted features from vibration signals and that from the shock pulse method are compared. In this regard, a set of accelerated life tests on rolling element bearings were designed and performed. Both shock pulse signals and vibration signals of the under-test rolling element bearings were recorded. Then two models based on feed-forward neural-network are developed to predict the remaining useful life of rolling element bearings. In the first model, only extracted features from vibration signals are fed for remaining useful life prediction. In the second model, the extracted features from shock pulse method are fed too. The results show that using shock pulse method-based features improves the accuracy of remaining useful life estimation. Also, using the health indicators extracted from vibration analysis and shock pulse method leads to a better estimating of the degradation behavior.