Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons on C-MAPSS Datasets



Published Jul 8, 2014
Emmanuel Ramasso


Prognostics and Health Management (PHM) is a multidisciplinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system’s health state. A health index (HI) is estimated to feed a data-driven PHM solution developed to predict the remaining useful life (RUL). In this paper, the values taken by an HI are assumed imprecise (IHI). An IHI is interpreted as a planar figure called polygon and a case-based reasoning (CBR) approach is adapted to estimate the RUL. This adaptation makes use of computational geometry tools in order to estimate the nearest cases to a given testing instance. The proposed algorithm called RULCLIPPER is assessed and compared on datasets generated by the NASA’s turbofan simulator (C-MAPSS) including the four turbofan testing datasets and the two testing datasets of the PHM’08 data challenge. These datasets represent 1360 testing instances and cover different realistic and difficult cases considering operating conditions and fault modes with unknown characteristics. The problem of feature selection, health index estimation, RUL fusion and ensembles are also tackled. The proposed algorithm is shown to be efficient with few parameter tuning on all datasets.

How to Cite

Ramasso, E. (2014). Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons on C-MAPSS Datasets. PHM Society European Conference, 2(1).
Abstract 1623 | PDF Downloads 552



Data Uncertainty, Instance-based learning, Prediction method, Pattern matching, Geometry, CMAPSS datasets

Al-Salah, T., Zein-Sabatto, S., & Bodruzzaman, M. (2012). Decision fusion software system for turbine engine fault diagnostics. In Southeastcon, 2012 proceedings of ieee (p. 1-6).
Beer, M., Ferson, S., & Kreinovich, V. (2013). Imprecise probabilities in engineering analyses. Mechanical Systems and Signal Processing.
Bentley, J., & Ottmann, T. (1979). Algorithms for reporting and counting geometric intersections. IEEE Trans. Comput., C28, 643-647.
Chazelle, B., & Edelsbrunner, H. (1992). An optimal algorithm for intersecting line segments in the plane. J. Assoc. Comput. Mach, 39, 1-54.
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2011). Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, 58(9), 4353-4364.
Coble, J. (2010). Merging data sources to predict remaining useful life - an automated method to identify prognostic parameters (Unpublished doctoral dissertation). University of Tennessee, Knoxville.
El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2011). Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system. Microelectronics Reliability, 51(2), 310 - 320.
Filippov, A. (1950). An elementary proof of Jordan’s theorem. Uspekhi Mat. Nauk, 5, 173-176.
Greiner, G., & Hormann, K. (1998). Efficient clipping of arbitrary polygons. ACM Trans. on Graphics, 17, 71-83.
Heimes, F. (2008). Recurrent neural networks for remaining useful life estimation. In Ieee int. conf. on prognostics and health management.
Hu, C., Youn, B., Wang, P., & Yoon, J. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering and System Safety, 103, 120 - 135.
Javed, K., Gouriveau, R., & Zerhouni, N. (2013). Novel failure prognostics approach with dynamic thresholds for machine degradation. In Ieee industrial electronics conference.
Klir, G., & Wierman, M. (1999). Uncertainty-based information. elements of generalized information theory. In (chap. Studies in fuzzyness and soft computing). Physica-Verlag.
Kuncheva, L. I. (2004). Combining pattern classifiers: Methods and algorithms. Wiley-Interscience.
Liu, K., Gebraeel, N. Z., & Shi, J. (2013). A data-level fu-sion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Trans. on Automation Science and Engineering.
Longley, P., de Smith, M., & Goodchild, M. (2007). Geospatial analysis : A comprehensive guide to principles, techniques and software tools. Matador, Leicester.
Margalit, A., & Knott, G. (1989). An algorithm for computing the union, intersection or difference of two polygons. Computers & Graphics, 13, 167-183.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Int. conf. on prognostics and health management.
Peel, L. (2008). Data driven prognostics using a Kalman filter ensemble of neural network models. In Int. conf. on prognostics and health management.
Peng, T., He, J., Liu, Y., Saxena, A., Celaya, J., & Goebel, K. (2012). Integrated fatigue damage diagnosis and prognosis under uncertainties. In Annual conference of prognostics and health management.
Powers, D. (2011). Evaluation: From precision, recall and F-factor to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2, 37-63.
Ramasso, E., & Denoeux, T. (2013). Making use of partial knowledge about hidden states in hidden Markov models: an approach based on belief functions. IEEE Transactions on Fuzzy Systems, 22(2), 395-405.
Ramasso, E., & Gouriveau, R. (2013). RUL estimation by classification of predictions: an approach based on a neuro-fuzzy system and theory of belief functions. IEEE Transactions on Reliability, Accepted.
Ramasso, E., Rombaut, M., & Zerhouni, N. (2013). Joint prediction of observations and states in time-series based on belief functions. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, 43, 37-50.
Riad, A., Elminir, H., & Elattar, H. (2010). Evaluation of neural networks in the subject of prognostics as compared to linear regression model. International Journal of Engineering & Technology, 10, 52-58.
Rigaux, P., Scholl, M., & Voisard, A. (2002). Spatial databases with application to gis (E. Science, Ed.). Kauffman Publishers.
Rosen, K. (2004). Handbook of discrete and computational geometry, second edition (J. E. Goodman & J. O’Rourke, Eds.). Chapman and Hall/CRC.
Sarkar, S., Jin, X., & Ray, A. (2011). Data-driven fault detection in aircraft engines with noisy sensor measurements. Journal of Engineering for Gas Turbines and Power, 133, 081602.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In Int. conf. on prognostics and health management (p. 1-17).
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto-failure simulation. In Int. conf. on prognostics and health management (p. 1-9). Denver, CO, USA.
Saxena, A., Wu, B., & Vachtsevanos, G. (2005). Integrated diagnosis and prognosis architecture for fleet vehicles using dynamic case-based reasoning. In Autotestcon (p. 96-102).
Serir, L., Ramasso, E., & Zerhouni, N. (2012). E2GKpro: An evidential evolving multi-modeling approach for system behavior prediction with applications. Mechanical Systems and Signal Processing. doi: 10.1016/j.ymssp.2012.06.023
Vachtsevanos, G. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken, NJ.
Vatti, B. R. (1992). A generic solution to polygon clipping. Communications of the ACM, 35, 56-63.
Wang, P., Youn, B., & Hu, C. (2012). A generic probabilistic framework for structural health prognostics and uncertainty management. Mechanical Systems and Signal Processing, 28, 622 - 637.
Wang, T. (2010). Trajectory similarity based prediction for remaining useful life estimation (Unpublished doctoral dissertation). University of Cincinnati.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similaritybased prognostics approach for remaining useful life estimation of engineered systems. In Int. conf. on prognostics and health management (p. 1-6).
Zein-Sabatto, S., Bodruzzaman, J., & Mikhail, M. (2013). Statistical approach to online prognostics of turbine engine components. In Southeastcon, 2013 proceedings of ieee (p. 1-6).
Technical Papers

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.