| Popis: |
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. |
