{"title":"An Optimal Bayesian Maintenance Policy for a Partially Observable System Subject to Two Failure Modes","authors":"Akram Khaleghei Ghosheh Balagh, Viliam Makis, Leila Jafari","volume":93,"journal":"International Journal of Mechanical and Mechatronics Engineering","pagesStart":1543,"pagesEnd":1548,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9999273","abstract":"
In this paper, we present a new maintenance model
\r\nfor a partially observable system subject to two failure modes,
\r\nnamely a catastrophic failure and a failure due to the system
\r\ndegradation. The system is subject to condition monitoring and the
\r\ndegradation process is described by a hidden Markov model. A
\r\ncost-optimal Bayesian control policy is developed for maintaining
\r\nthe system. The control problem is formulated in the semi-Markov
\r\ndecision process framework. An effective computational algorithm is
\r\ndeveloped, illustrated by a numerical example.<\/p>\r\n","references":"[1] W. Jianmou and V. Makis, \"Economic and economic-statistical design of\r\na chi-square chart for cbm,\u201d European Journal of Operation Research,\r\nvol. 188, no. 2, pp. 516\u2013529, 2008.\r\n[2] B. Liu, \"Vibration data monitoring and design of multivariate ewma\r\nchart for cbm,\u201d Ph.D. Dissertation, University of Totonto, 2006.\r\n[3] M. Kim and V. Makis, \"Joint optimization of sampling and control of\r\npartially observable failing systems,\u201d Operation Research, vol. 61, no. 3,\r\npp. 77\u2013790, 2013.\r\n[4] V. Makis, \"Multivariate bayesian control chart,\u201d Operation Research,\r\nvol. 56, pp. 487\u2013496, 2008.\r\n[5] \u2014\u2014, \"Multivariate bayesian process control for a finite production run,\u201d\r\nEuropean Journal of Operation Research, vol. 194, pp. 795\u2013806, 2009.\r\n[6] R. Jiang and V. Makis, \"Arl criterion in bayesian process control using\r\nhidden markov model,\u201d IEEE, 2009.\r\n[7] Z. Yin and V. Makis, \"Economic and economic-statistical design of a\r\nmultivariate bayesian control chart for condition-based maintenance,\u201d\r\nIMA Journal of Management Mathematics, vol. 22, pp. 47\u201363, 2010.\r\n[8] R. Jiang, J. Yu, and V. Makis, \"Optimal bayesian estimation and\r\ncontrol scheme for gear shaft fault detection,\u201d Computers & Industrial\r\nEngineering, vol. 63, pp. 754\u2013762, 2012.\r\n[9] M. Kim, R. Jiang, V. Makis, and L. C., \"Optimal bayesian fault\r\nprediction scheme for a partially observable system subject to random\r\nfailure,\u201d European Journal of Operational Research, vol. 214, pp.\r\n331\u2013339, 2011.\r\n[10] R. Jiang, M. Kim, V. Makis, and C. Lee, \"A bayesian model and\r\nnumerical algorithm for cbm availability maximization,\u201d Annals of\r\nOperations Research, vol. 196, no. 1, pp. 333\u2013348, 2012.\r\n[11] X. Liu, J. Li, K. Al-Khalifa, A. Hamouda, andW. Coit, \"Condition-based\r\nmaintenance for continuously monitored degrading systems with\r\nmultiple failure modes,\u201d IIE Transactions, vol. 45, no. 4, pp. 422\u201335,\r\n2013.\r\n[12] I. Tumer and E. Huff, \"Analysis of triaxial vibration data for health\r\nmonitoring of helicopter gearboxes,\u201d Journal of Vibration and Acoustics,\r\nvol. 125, pp. 120\u2013128, 2003.\r\n[13] V. Makis, J. Wu, and Y. Gao, \"An application of dpca to oil data for\r\ncbm modeling,\u201d European Journal of Operational Research, vol. 174,\r\nno. 1, pp. 112\u2013123, 2006.\r\n[14] X. Wang, V. Makis, and Y. M, \"A wavelet approach to fault diagnosis of\r\nagearbox under varying load conditions,\u201d Journal of Soundand Vibration,\r\nvol. 329, p. 15701585, 2010.\r\n[15] R. Jiang, \"System availability maximization and residual life predictioni\r\nunder partial observations,\u201d Ph.D. thesis, University of Toroto, 2011.\r\n[16] R. Palivonaite, K. Lukoseviciute, and M. Ragulskis, \"Algebraic\r\nsegmentaion of short nonstationary time series based on evolutionary\r\nprediction algorithms,\u201d Neurocomputing, vol. 121, pp. 354\u2013364, 2013.\r\n[17] H. Aksoy, A. Gedikli, N. Unal, and A. Kehagias, \"Fast segmentation\r\nalgoirthms for long hydrometeorological time series,\u201d Hydrological\r\nProcesses, vol. 22, pp. 4600\u20134608, 2008.\r\n[18] J. Yang and V. Makis, \"Dynamic response of residual to external\r\ndeviations in a controlled production process,\u201d Technometrics, vol. 42,\r\npp. 290\u2013299, 2000.\r\n[19] A. Snoussi, M. Ghourabi, and M. Limam, \"On spc for short\r\nrun autocorrelated data,\u201d Communication in Statistics-Simulation and\r\nComputation, vol. 34, pp. 219\u2013234, 2005.\r\n[20] M. Ghourabi and M. Limam, \"Residual responses to change patterns of\r\nautocorrelated processes,\u201d Journal of Applied Statistics, vol. 34, no. 7,\r\npp. 785\u2013798, 2007.\r\n[21] V. Makis and J. X, \"Optimal replacement under partial observations,\u201d\r\nMathematics of Operation Research, vol. 28, no. 2, pp. 382\u2013394, 2003.\r\n[22] S. Provost and E. Rudiuk, \"The exact distribution of indefinite quadratic\r\nforms in noncentral normal vectors,\u201d Annals of the Institute of Statistical\r\nMathematics, vol. 48, no. 1, pp. 381\u2013394, 1996.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 93, 2014"}