Engine remaining useful life prediction based on trajectory similarity

The traditional remaining useful life prediction methods need to study the mechanism failure of equipment and the vibration signals can easily be submerged by the noise in the actual operation, in order to solve these problems, the methods of Trajectory similarity based prediction (TSBP) and condition monitoring based on lubricant information are proposed in this paper. The gradient model of lubricant data information which is processed by principal component analysis (PCA) is used to monitor equipment status. Additionally, degradation trajectory abstraction procedure and similarity evaluation procedure are studied in detail. Finally, the both studies are combined for the research of engine remaining useful life prediction and case study proves the simplicity and effectiveness of this method.