Abstract
This paper presents a local pattern-based method that addresses system prognosis. It also details a successful application to complex vacuum pumping systems. More precisely, using historical vibratory data, we first model the behavior of systems by extracting a given type of episode rules, namely First Local Maximum episode rules (FLM-rules). A subset of the extracted FLM-rules is then selected in order to further predict pumping system failures in a vibratory datastream context. The results that we got for production data are very encouraging as we predict failures with a good time scale precision. We are now deploying our solution for a customer of the semi-conductor market.
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
Abu-Hanna, A., Lucas, P.J.F.: Prognostic models in medicine: AI and statistical approaches. Jr. of Methods of Information in Medicine 40, 1–5 (2001)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the 11th Intl. Conf. on Data Engineering, pp. 3–14 (1995)
Cho, C., Zheng, Y., Chen, A.L.P.: Continuously Matching Episode Rules for Predicting Future Events over Event Streams. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM 2007. LNCS, vol. 4505, pp. 884–891. Springer, Heidelberg (2007)
Fiot, C., Masseglia, F., Laurent, A., Teisseire, M.: Gradual trends in fuzzy sequential patterns. In: Proc. of the 12th Intl. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), pp. 456–463 (2008)
Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.S.: Mining frequent patterns in data streams at multiple time granularities. Next Generation Data Mining 212, 191–212 (2003)
Grabill, P., Brotherton, T., Berry, J., Grant, L.: The us army and national guard vibration management enhancement program: data analysis and statistical results. In: Annual proceeding of American helicopter society, vol. 58, pp. 105–119 (2002)
Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H.: TASA: Telecomunications Alarm Sequence Analyzer or: How to enjoy faults in your network. In: IEEE Network Operations and Management Symposium, pp. 520–529 (1996)
Harding, J.A., Shahbaz, M., Kusiak, A.: Data mining in manufacturing: a review. Jr. of Manufacturing Science and Engineering 128(4), 969–976 (2006)
Letourneau, S., Famili, F., Matwin, S.: Data mining for prediction of aircraft component replacement. IEEE Intelligent Systems and their Applications 14(6), 59–66 (1999)
Lalanne, C.: Vibrations aléatoires. Hermes Science (1999)
Le Normand, N., Boissiere, J., Meger, N., Valet, L.: Supply chain management by means of FLM-rules. In: 12th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 29–36 (2008)
Magoulas, G.D., Prentza, A.: Machine learning in medical applications. Jr. Machine Learning and Its Applications 2049, 300–307 (2001)
Meger, N., Rigotti, C.: Constraint-based mining of episode rules and optimal window sizes. In: Chin, W.-N. (ed.) APLAS 2004. LNCS, vol. 3302, pp. 313–324. Springer, Heidelberg (2004)
Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: Proc. of the 2nd Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 146–151 (1996)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Jr. of Data Mining and Knowledge Discovery 1(3), 259–298 (1997)
Raghavan, V., Hafez, A.: Dynamic data mining. In: Logananthara, R., Palm, G., Ali, M. (eds.) IEA/AIE 2000. LNCS (LNAI), vol. 1821, pp. 220–229. Springer, Heidelberg (2000)
Schwabacher, M., Goebel, K.: A Survey of Artificial Intelligence for Prognostics. In: Working Notes of 2007 American Institute in Aeronautics and Astronautics Fall Symposium: AI for Prognostics (2007)
Jens, T.B.: Mechanical Vibration and Shock Measurements. Bruel & Kjaer (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martin, F., Méger, N., Galichet, S., Becourt, N. (2010). Episode Rule-Based Prognosis Applied to Complex Vacuum Pumping Systems Using Vibratory Data. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-14400-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14399-1
Online ISBN: 978-3-642-14400-4
eBook Packages: Computer ScienceComputer Science (R0)