Enhanced Trajectory Based Similarity Prediction with Uncertainty Quantification



Published Sep 29, 2014
Jack Lam Shankar Sankararaman Bryan Stewart


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.

How to Cite

Lam , J. ., Sankararaman, S. ., & Stewart, . B. . (2014). Enhanced Trajectory Based Similarity Prediction with Uncertainty Quantification. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2513
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data driven prognostics, Uncertainty Quantification

Botev, Z. I., Grotowski, J. F., & Kroese, D. P. (2010). Kernel density estimation via diffusion. The Annals of Statistics, 39(5), 2916-2957.

Dallachiesa, M., Nushi, B., Mirylenka, K., & Palpanas, T. (2012). Uncertain Time-Series Similarity: Return to the Basics. University of Trento.

Fonseca, J., Friswell, M. I., Mottershead, J. E., Lees, A. W., & Adhikari, S. (2005). Uncertainty Quantification using Maximum Likelihood: Experimental V alidation. 46th
AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference. Austin.

Giusti, G. R. (2013). An Empirical Comparison of Dissimilarity Measures for Time Series Classification. Brazilian Conference on Intelligent Systems (BRACIS). Fortaleza.

Gouriveau, R., Ramasso, E., & Zerhouni, N. (2013). Strategies to face imbalanced and unlabelled data in PHM applications. Chemical Engineering Transactions(33), 115-120.

Guo, H., Gerokostopoulos, A., Liao, H., & Niu, P. (2013). Modeling and Analysis for Degradation with an Initiation Time. Reliability and Maintainability Symposium.

Lei, H., & Govindaraju, V. (2004). Matching and Retrieving Sequential Patterns Under Regression. IEEE/WIC/ACM International Conference on Web Intelligence.

Otey, M. E., & Parthasarathy, S. (2004). A Dissimilarity Measure for Comparing Subsets of Data: Application to Multivariate Time Series. Department of Computer Science and Engineering, The Ohio State University.

Salvador, S., & Chan, P. (2007). FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. Intelligent Data Analysis.

Sankararaman, S. (2014). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, In press.

Sankararaman, S., & Goebel, K. (2013). Why is the Remaining Useful Life Prediction Uncertain? Annual Conference of the Prognostics and Health Management Society 2013.

Sankararaman, S., Daigle, M., & Goebel, K. (2014). Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods. IEEE Transactions on Reliability, 603- 619.

Saxena, A., & Goebel, K. (2008). C-MAPSS Data Set. NASA Ames Prognostics Data Repository.

Saxena, A., & Goebel, K. (2008). PHM08 Challenge Data Description. Denver: 1st International Conference on Prognostics and Health Management.

Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., et al. (2008). Metrics for Evaluating Performance of Prognostic Techniques.
Prognostics and Health Management (PHM).

Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009). On applying the prognostic performance metrics. Prognostics and Health Management Society.

Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. Ist International Conference on Prognostics and Health Management (PHM08). Denver.

Wang, P., & Coit, D. W. (2007). Reliability Assessment Based on Degradation Modeling with Random or Uncertain Failure Threshold. Reliability and Maintainability Symposium. Orlando, FL.

Wang, T. (2013). Trajectory Based prediction for Remaining Useful Life Estimation. Cincinnati: University of Cincinnati.

Yu, P., Yong, X., Datong, L., & Xiyuan, P. (2012). Sensor Selection with Grey Correlation Analysis for Remaining Useful Life Evaluation. PHM Society Conference.
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