NSWC Port Hueneme Division

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.

Many modern systems are themselves composed of smaller subsystems, which are composed of individual parts. Most prognostics algorithms attempt to provide a Remaining Useful Life (RUL) value for lower level components. Since the RUL prediction would allow for a replacement component to be requisitioned prior to a failure, it is anticipated the operational availability will be increased due to the reduction in system downtime attributed to logistics delay. For systems where logistics time is the major contributor to downtime, prognostics may yield large gains in operational availability. This paper provides an analysis of a hypothetical system that implements a prognostics algorithm and examines the system-level gains for different RUL prediction values. The analysis uses the BlockSim and a Monte Carlo-based simulation tool that is able to take multiple redundancies into account when calculating metrics. Finally, two metrics are proposed to help quantify the benefit of prognostics: Relative Downtime Reduction and Relative Availability Gain.