Equipment diagnostics based on comparison of past abnormal behaviors using a big data platform

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Published Jul 5, 2016
Carole MAI Robin CHEVALIER

Abstract

Nuclear monitoring systems provide an increasing amount of realtime measurements to analyse equipment condition. Storage, retrieval, and effective use of information must therefore be made as efficient as possible in order to achieve reliability goals and additional reductions in operating costs. Combining them with other data sources like equipment characteristics, maintenance logs and previous expertise reports will help make better diagnostics while minimizing the time dedicated to an analysis hence leading to better maintenance decisions. This paper presents an overview of the project under study at EDF to develop a case based reasoning platform enabling experts to efficiently retrieve similarities between past events and the current situation. Several technical barriers are encountered such as the comparison of multidimensional time series, of textual information and the extraction of signal features. The problem is here framed as a pattern classification problem where the classes correspond to equipment faults. Similarity criteria have been defined and evaluated against nuclear power plants data for the diagnosis of abnormal patterns on critical equipment. The classification results are compared with service and expertise reports using clustering and classification algorithms. The prospect of this diagnosis is to support the adjustment of maintenance schedule by estimating the remaining useful life of critical equipment.

How to Cite

MAI, C., & CHEVALIER, R. (2016). Equipment diagnostics based on comparison of past abnormal behaviors using a big data platform. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1602
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Keywords

case based reasoning, Pattern classification, diagnostic

References
Aamodt, A., & Plaza, E. (1994). Case-based reasoning; foundational issues, methodological variations, and system approaches. AI COMMUNICATIONS, 7(1), 39–59. Condition monitoring and diagnostics of machines – vocabulary (Standard). (2012, September). International Organization for Standardization (ISO).
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Hierarchical clustering. In Cluster analysis (pp. 71–110). doi: 10.1002/9780470977811.ch4
Jardine, A. K., Lin, D., & Banjevic, D. (2006, oct). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi: 10.1016/j.ymssp.2005.09.012
Lesot, M.-J., Rifqi, M., & Benhadda, H. (2008, December). Similarity measures for binary and numerical data: a survey. International Journal of Knowledge Engineering and Soft Data Paradigms, 1(1), 63-84. doi: 10.1504/IJKESDP.2009.021985
Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, MA, USA: MIT Press.
Senin, P. (2008, December). Dynamic Time Warping Algorithm Review (Tech. Rep. No. CSDL-08-04). Department of Information and Computer Sciences, University of Hawaii, Honolulu, Hawaii 96822.
Song, Q. (2001). Ecm - a novel on-line, evolving clustering method and its applications. In In m. i. posner (ed.), foundations of cognitive science (pp. 631–682). The MIT Press.
Stanfill, C., & Waltz, D. (1986, December). Toward memorybased reasoning. Commun. ACM, 29(12), 1213–1228. doi: 10.1145/7902.7906
Todorov, Y., Feller, S., & Chevalier, R. (2015, July). Making the investigation of huge data archives possible in an industrial context an intuitive way of finding non-typical patterns in a time series haystack. In Informatics in control, automation and robotics (icinco), 2015 12th international conference on (Vol. 01, p. 569-581).
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012, feb). Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2), 275–309. doi: 10.1007/s10618-012-0250-5
Section
Technical Papers