Abstract
Data-driven prognostic for system health management represents an emerging and challenging application of data mining. The objective is to develop data-driven prognostic models to predict the likelihood of a component failure and estimate the remaining useful lifetime. Many models developed using techniques from data mining and machine learning can detect the precursors of a failure but sometimes fail to precisely predict time to failure. This paper attempts to address this problem by proposing a novel approach to find reliable patterns for prognostics. A reliable pattern can predict state transitions from current situation to upcoming failures and therefore help better estimate the time to failure. Using techniques from data mining and time-series analysis, we developed a KDD methodology for discovering reliable patterns from multi-stream time-series databases. The techniques have been applied to a real-world application: train prognostics. This paper reports the developed methodology along with preliminary results obtained on prognostics of wheel failures on train.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schwabacher, W., Goebel, K.: A survey of artificial intelligence for prognostics. In: The AAAI fall Symposium on Artificial Intelligence for Prognostics, pp. 107–114. AAAI Press, Arlington (2007)
Brown, E.R., McCollom, N.N., Moore, E.E., Hess, A.: Prognostics and health management: a data-driven approach to supporting the F-35 lightning II. In: IEEE Aerospace Conference (2007)
Camci, F., Valentine, G.S., Navarra, K.: Methodologies for integration of PHM systems with maintenance data. In: IEEE Aerospace Conference (2007)
Daw, C.S., Finney, E.A., Tracy, E.R.: A review of symbolic analysis of experimental data. Review of Scientific Instruments 74(2), 915–930 (2003)
Przytula, E.W., Chol, A.: Reasoning framework for diagnosis and prognosis. In: IEEE Aerospace Conference (2007)
Usynin, A., Hines, J.W., Urmanov, A.: Formulation of prognostics requirements. In: IEEE Aerospace Conference (2007)
Luo, M., Wang, D., Pham, M., Low, C.B., Zhang, J.B., Zhang, D.H., Zhao, Y.Z.: Model-based fault diagnosis/prognosis for wheeled mobile robots: a review. In: The 31st Annual Conference of IEEE Industry Electronics Society 2005, New York (2005)
Létourneau, S., Yang, C., Drummond, C., Scarlett, E., Valdés, J., Zaluski, M.: A domain independent data mining methodology for prognostics. In: The 59th Meeting of the Machinery Failure Prevention Technology Society (2005)
Yang, C., Létourneau, S.: Learning to predict train wheel failures. In: The 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005), pp. 516–525 (2005)
Keogh, E., Lin, J., Fu, A., Herle, H.V.: Finding the unusual medical time series: algorithms and applications. IEEE Transactions on Information Technology in Biomedicine (2005)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: A survey and novel approach. In: Kandel, Bunke (eds.) Data Mining in Time Series Databases, pp. 1–44. World Scientific Publishing, Singapore (2004)
Mielikäinen, T., Mannila, H.: The pattern ordering problem. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 327–338. Springer, Heidelberg (2003)
Xin, D., Cheng, H., Yan, X., Han, J.: Extracting redundancy-aware top-k patterns. In: ACM KDD 2006, Philadelphia, Pennsylvania, USA, pp. 444–453 (2006)
Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., Wootton, J.C.: Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignments. Science 262(8), 208–214 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, C., Létourneau, S. (2011). Discovering Patterns for Prognostics: A Case Study in Prognostics of Train Wheels. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-21822-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
Online ISBN: 978-3-642-21822-4
eBook Packages: Computer ScienceComputer Science (R0)