Anomaly Prediction: Successive Analysis for Healthy State Based on the Expansion to Remaining Useful Life

Abstract The prediction of remaining useful life is the key to realize the condition-based maintenance. However, Existing studies on remaining useful life cannot effectively cope with the scenarios with extremely high requirement to stability. In this paper, the conception of anomaly prediction is introduced to solve the problem by expanding the remaining useful life to healthy stage to describe and predict the equipment condition at this stage. To obtain more reasonable health indicator, inverse number is firstly introduced into the feature selection. On this basis, a novel method is put forward, which consists of center optimization, boundary adjustment and regressive analysis, in order to realize the anomaly prediction and fills the gap of the absence to remaining life research at healthy stage. Two validations are subsequently implemented with the open dataset of XJTU-SY. The experimental results and comparison analysis demonstrate the feasibility and the superiority of proposed method, respectively. The dissection to the remaining useful life based anomaly prediction indicates that the related research is essential and of great significant to the condition-based maintenance.