SVM-PHM: A Novel Method for Remaining Useful Life Prediction

The Remaining Useful Life(RUL) prediction of the equipment plays a significant role in maintenance management. The accurate RUL prediction based on the current and previous health condition of the equipment is essential to make a timely maintenance decision for failure avoidance. In this paper, we presented a novel RUL forecasting method of Proportional Hazards Model (PHM) assembled with Support Vector Machine (SVM). In this method, we employed SVM to identify abnormal data and regress raw data. A case study is presented, and the performances of RUL prediction of PHM and SVN-PHM are examined.