Enhanced Trajectory Based Similarity Prediction with Uncertainty Quantification

Abstract

Today, data driven prognostics acquires historic data to generate degradation path and estimate the Remaining Useful Life (RUL) of a system. A successful methodology, Trajectory Similarity Based Prediction (TSBP) that details the process of predicting the system RUL and evaluating the performance metrics of the estimate was proposed in 2008. Two essential components of TSBP identified for potential improvement include 1) a distance or similarity measure that is capable of determining which degradation model the testing data is most similar to and 2) computation of uncertainty in the remaining useful life prediction, instead of a point estimate. In this paper, the Trajectory Based Similarity Prediction approach is evaluated to include Similarity Linear Regression (SLR) based on Pearson Correlation and Dynamic Time Warping (DTW) for determining the degradation models that are most similar to the testing data. A computational approach for uncertainty quantification is implemented using the principle of weighted kernel density estimation in order to quantify the uncertainty in the remaining useful life prediction. The revised approach is measured against the same dataset and performance metrics evaluation method used in the original TBSP approach. The result is documented and discussed in the paper. Future research is expected to augment TSBP methodology with higher accuracy and stronger anticipation of uncertainty quantification.