Abstract
This paper demonstrates the implementation of Logical Analysis of Data (LAD) methodology in the field of prognostics in Condition Based Maintenance (CBM). In this paper the LAD classification methodology, based on Sensitive Discriminating and Equipartitioning methods for data binarization, Mixed Integer Linear Programming (MILP) and Hybrid Greedy methods for pattern generation, is used. Using the generated patterns, two methods of calculating the survival function are introduced. The methodology is applied on Prognostics and Health Management Challenge dataset, which is a condition monitoring dataset provided by NASA Ames Prognostics Data Repository. The results obtained by using LAD methodology, are compared with that obtained by using the Proportional Hazards Model (PHM).
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Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20(7), 1483–1510 (2006)
Crama, Y., Hammer, P.L., Ibaraki, T.: Cause-effect relationships and partially defined boolean functions. Annals of Operations Research, 16(1) 16(1), 299–326 (1988)
Bennane, A., Yacout, S.: LAD-CBM; new data processing tool for diagnosis and prognosis in condition-based maintenance. Journal of Intelligent Manufacturing 23(2), 265–275 (2012)
Mortada, M., Carroll, T., Yacout, S., Lakis, A.: Rogue components: Their effect and control using logical analysis of data. Journal of Intelligent Manufacturing 23(2), 289–302 (2012)
Mortada, M., Yacout, S., Lakis, A.: Diagnosis of rotor bearings using logical analysis of data. Journal of Quality in Maintenance Engineering 17(4), 371–397 (2011)
Yacout, S.: Fault detection and diagnosis for condition based maintenance using the logical analysis of data. In: 2010 40th International Conference on Presented at Computers and Industrial Engineering (CIE) (2010)
Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A.: Logical analysis of numerical data. Mathematical Programming 79(1), 163–190 (1997)
Kotsiantis, S., Kanellopoulos, D.: Discretization techniques: a recent survey. GESTS Int. Transact. Comput. Sci. Eng. 32, 47–58 (2006)
Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: An enabling technique. Data Mining and Knowledge Discovery 6(4), 393–423 (2002)
Alexe, S., Hammer, P.L.: Accelerated algorithm for pattern detection in logical analysis of data. Discrete Applied Mathematics 154(7), 1050–1063 (2006)
Hammer, P.L., Kogan, A., Simeone, B., Szedmak, S.: Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics 144(1-2), 79–102 (2004)
Ryoo, H.S.: MILP approach to pattern generation in logical analysis of data. Discrete Applied Mathematics 157(4), 749–761 (2009)
Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A., Mayoraz, E., Muchnik, I.: An implementation of logical analysis of data. IEEE Transactions on Knowledge and Data Engineering 12(2), 292–306 (2000)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53(282), 457–481 (1958)
Banjevic, D., Jardine, A.K.S.: Remaining useful life in condition based maintenance: Is it useful? In: Proceedings of MIMAR 2007, the 6th IMA International Conference, pp. 7–12 (2007)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11(1), 86–92 (1940)
Boros, E., Crama, Y., Hammer, P., Ibaraki, T., Kogan, A., Makino, K.: Logical analysis of Data: Classification with justification. Rutcor Research Report, RRR 5-2009 (2009)
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Ghasemi, A., Esmaeili, S., Yacout, S. (2013). Equipment’s Prognostics Using Logical Analysis of Data. In: Emmanouilidis, C., Taisch, M., Kiritsis, D. (eds) Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services. APMS 2012. IFIP Advances in Information and Communication Technology, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40361-3_31
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DOI: https://doi.org/10.1007/978-3-642-40361-3_31
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