Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines

摘要

In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. To test the system's RUL, degradation speed is evaluated by computing the minimal distance based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data on a different time horizon. The final RUL of a specific component can be estimated and global RUL information can be obtained by aggregating the multiple RUL estimations using a density estimation method.