相似性(几何)
数据挖掘
弹道
计算机科学
动态时间归整
核(代数)
核密度估计
预言
人工智能
机器学习
作者
Jack Lam,Shankar Sankararaman,Bryan Stewart
出处
期刊:Annual Conference of the PHM Society
日期:2014-10-02
卷期号:6 (1)
被引量:3
标识
DOI:10.36001/phmconf.2014.v6i1.2513
摘要
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.
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