Real-time bearing residual life prediction based on status excursion

This paper deals with a new scheme for the prediction of a bearing's residual life based on the individual relative excursion compared with population. Sometimes we cannot establish the model of degradation trajectory. Nevertheless, the randomness, existing within the actual running of individual, can cause the individual relative excursion compared with population. The excursion well reflects the status characteristic of individual. In that case, this paper proposes an open and data-driven based status assessment method to obtain the individual status offset. After that we use the status deviation to establish the reliability correction model based on state offset, and then update the failure distribution to assess the real-time reliability, and ultimately accomplish real-time life prediction.