Remaining useful life estimation – A review on the statistical data driven approaches
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aalen, 1995, Phase type distribution in survival analysis, Scandinavian Journal of Statistics, 22, 447
Aalen, 2008
Abdel-Hameed, 1975, A Gamma wear process, IEEE Transactions on Reliability, 24, 152, 10.1109/TR.1975.5215123
Altay, 2006, OR/MS research in disaster operations management, European Journal of Operational Research, 175, 475, 10.1016/j.ejor.2005.05.016
Azimi, 2005, Offline and online identification of hidden semi-Markov models, IEEE Transactions on Signal Processing, 53, 2658, 10.1109/TSP.2005.850344
Bae, 2004, A nonlinear random-coefficients model for degradation, Technometrics, 46, 460, 10.1198/004017004000000464
Bae, 2007, Degradation models and implied lifetime distributions, Reliability Engineering and System Safety, 92, 601, 10.1016/j.ress.2006.02.002
Bagdonavicius, 2000, Estimation in degradation models with explanatory variables, Lifetime Data Analysis, 7, 85, 10.1023/A:1009629311100
Baker, 1994, Review of delay-time OR modelling of engineering aspects of maintenance, European Journal of Operational Research, 73, 407, 10.1016/0377-2217(94)90234-8
Balka, 2009, Review and implementation of cure models based on first hitting times for Wiener processes, Lifetime Data Analysis, 15, 147, 10.1007/s10985-008-9108-y
Banjevic, 2009, Remaining useful life in theory and practice, Metrika, 69, 337, 10.1007/s00184-008-0220-5
Banjevic, 2006, Calculation of reliability function and remaining useful life for a Markov failure time process, IMA Journal of Management Mathematics, 17, 115, 10.1093/imaman/dpi029
Banjevic, 2001, A control limit policy software for condition-based maintenance, INFOR, 39, 32
Baruah, 2005, HMMs for diagnostics and prognostics in machining processes, International Journal of Production Research, 43, 1275, 10.1080/00207540412331327727
Batzel, 2009, Prognostic health management of aircraft power generators, IEEE Transactions on Aerospace and Electronic Systems, 45, 473, 10.1109/TAES.2009.5089535
Bunks, 2000, Condition-based maintenance of machines using hidden markov models, Mechanical Systems and Signal Processing, 14, 597, 10.1006/mssp.2000.1309
Buonocore, A., Caputo, L., Pirpzzi, E., Ricciardi, L.M., in press. The first passage time problem for Gauss-diffusion processes: Algorithmic approaches and applications to LIF neuronal model. Methodology and Computing in Applied Probability. doi:10.1007/s11009-009-9132-8.
Camci, 2010, Health state estimation and prognostics in machining processes, IEEE Transactions on Automation Science and Engineering, 7, 581, 10.1109/TASE.2009.2038170
Carr, 2008, A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 222, 47, 10.1243/1748006XJRR76
Carr, 2010, Modeling failure modes for residual life prediction using stochastic filtering theory, IEEE Transactions on Reliability, 59, 346, 10.1109/TR.2010.2044607
Chakraborty, 2009, Residual-life estimation for components with non-symmetric priors, IIE Transactions, 41, 372, 10.1080/07408170802369409
Cheng, S., Pecht, M., 2009. A fusion prognostics method for remaining useful life prediction of electronic products. In: 5th Annual IEEE Conference on Automation Science and Engineering, Bangalore, India, August, pp. 102–107.
Chhikara, 1977, The inverse Gaussian distribution as a lifetime model, Technometrics, 19, 461, 10.2307/1267886
Chinnam, 2009, Autonomous diagnostics and prognostics in machining process through competitive learning-driven HMM-based clustering, International Journal of Production Research, 47, 6739, 10.1080/00207540802232930
Cho, 1991, A survey of maintenance models for multi-unit systems, European Journal of Operational Research, 51, 1, 10.1016/0377-2217(91)90141-H
Christer, A.H., Wang, W., Sharp, J.M., 1995. A model of condition monitoring by stochastic filtering. In: Rao, B.K.N., Moore, T.N., Jeswiet, J., (Eds.), Proceedings of COMADEM 95, Kingston, Canada, pp. 329–336.
Christer, 1997, A state space condition monitoring model for furnace erosion prediction and replacement, European Journal of Operational Research, 101, 1, 10.1016/S0377-2217(97)00132-X
Cox, 1972, Regression models and life-tables (with discussion), Journal of the Royal Statistical Society, Series B (Methodological), 34, 187, 10.1111/j.2517-6161.1972.tb00899.x
Cox, 1965
Crowder, 2007, On a scheme for predictive maintenance, European Journal of Operational Research, 176, 1713, 10.1016/j.ejor.2005.10.051
Cui, 2004, Sequential inspection strategy for multiple systems under availability requirement, European Journal of Operational Research, 155, 170, 10.1016/S0377-2217(02)00822-6
Delia, 2006, Replacement times and costs in a degrading system with several types of failure: The case of phase-type holding times, European Journal of Operational Research, 175, 1193, 10.1016/j.ejor.2005.06.027
Delia, 2008, A maintenance model with failures and inspection following Markovian arrival processes and two repair modes, European Journal of Operational Research, 186, 694, 10.1016/j.ejor.2007.02.009
Dieulle, 2003, Sequential condition-based maintenance scheduling for a deteriorating system, European Journal of Operational Research, 150, 451, 10.1016/S0377-2217(02)00593-3
Doksum, 1992, Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution, Technometrics, 34, 74, 10.2307/1269554
Doksum, 1995, Gaussian models for degradation processes. Part I: Methods for the analysis of biomarker data, Lifetime Data Analysis, 1, 131, 10.1007/BF00985763
Dong, 2008, A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM), Science in China Series F: Information Sciences, 51, 1291, 10.1007/s11432-008-0111-4
Dong, 2007, Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis, European Journal of Operational Research, 178, 858, 10.1016/j.ejor.2006.01.041
Dong, 2007, A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology, Mechanical Systems and Signal Processing, 21, 2248, 10.1016/j.ymssp.2006.10.001
Dong, 2006, Equipment health diagnosis and prognosis using hidden semi-Markov models, The International Journal of Advanced Manufacturing Technology, 30, 738, 10.1007/s00170-005-0111-0
Doyle, 2009, Justification for the next generation of maintenance modelling techniques, Journal of the Operational Research Society, 60, 461, 10.1057/palgrave.jors.2602561
Dragomir, E., Gouriveau, R., Dragomir, F., Minca, E., Zerhouni, N., 2009. Review of prognostic problem in condition-based maintenance. In: European Control Conference, ECC’09, Budapest, Hungary.
Ebrahimi, 2005, System reliability based on diffusion models for fatigue crack growth, Naval Research Logistics, 52, 46, 10.1002/nav.20050
Ebrahimi, 2009, The mean function of a repairable system that is subjected to an imperfect repair policy, IIE Transactions, 41, 57, 10.1080/07408170802322671
Elsayed, 2003, Mean residual life and optimal operating conditions for industrial furnace tubes
Elwany, 2008, Sensor-driven prognostic models for equipment replacement and spare parts inventory, IIE Transactions, 40, 629, 10.1080/07408170701730818
Elwany, 2009, Real-time estimation of mean remaining life using sensor-based degradation models, Journal of Manufacturing Science and Engineering, 131, 051005-1, 10.1115/1.3159045
Gašperin, 2011, Model-based prognostics of gear health using stochastic dynamic models, Mechanical Systems and Signal Processing, 25, 537, 10.1016/j.ymssp.2010.07.003
Gebraeel, 2006, Sensory-updated residual life distributions for components with exponential degradation patterns, IEEE Transactions on Automation Science and Engineering, 3, 382, 10.1109/TASE.2006.876609
Gebraeel, 2008, Prognostic degradation models for computing and updating residual life distributions in a time-varying environment, IEEE Transactions on Reliability, 57, 539, 10.1109/TR.2008.928245
Gebraeel, 2005, Residual-life distributions from component degradation signals: A Bayesian approach, IIE Transactions, 37, 543, 10.1080/07408170590929018
Gebraeel, 2009, Residual life predictions in the absence of prior degradation knowledge, IEEE Transactions on Reliability, 58, 106, 10.1109/TR.2008.2011659
Ghasemi, 2010, Evaluating the reliability function and the mean residual life for equipment with unobservable states, IEEE Transactions on Reliability, 59, 45, 10.1109/TR.2009.2034947
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., Sun Y., 2009a. A review on reliability models with covariates. In: Proceedings of the 4th World Congress on Engineering Asset Management, Athens, Greece, September.
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., Sun Y., 2009b. A review on degradation models in reliability analysis. In: Proceedings of the 4th World Congress on Engineering Asset Management, Athens, Greece, Sept.
Guédon, 1999, Computation methods for discrete hidden semi-Markov chains, Applied Stochastic Models in Business and Industry, 15, 195, 10.1002/(SICI)1526-4025(199907/09)15:3<195::AID-ASMB376>3.0.CO;2-F
Heng, 2009, Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, 23, 724, 10.1016/j.ymssp.2008.06.009
Jardine, 1997, Optimal replacement policy and the structure of software for condition-based maintenance, Journal of Quality Maintenance Engineering, 3, 109, 10.1108/13552519710167728
Jardine, 1999, Optimizing condition-based maintenance decisions for equipment subject to vibration monitoring, Quality in Maintenance Engineering, 5, 192, 10.1108/13552519910282647
Jardine, 2006, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20, 1483, 10.1016/j.ymssp.2005.09.012
Jardine, 2008, Repairable system reliability: Recent developments in CBM optimization, International Journal of Performability Engineering, 4, 205
Jazwinski, 1970
Kaiser, 2009, Predictive maintenance management using sensor-based degradation models, IEEE Transactions on Systems, Man, Cybernetics – Part A: Systems and Humans, 39, 840, 10.1109/TSMCA.2009.2016429
Kalbfleisch, 2002
Kaplan, 1958, Nonparametric estimation from incomplete observations, Journal of the American Statistical Association, 53, 457, 10.2307/2281868
Kharoufeh, 2003, Explicit results for wear processes in a Markovian environment, Operations Research Letters, 31, 237, 10.1016/S0167-6377(02)00229-8
Kharoufeh, 2005, Stochastic models for degradation-based reliability, IIE Transactions, 37, 533, 10.1080/07408170590929009
Kharoufeh, 2009, On a Markov-modulated shock and wear process, Naval Research Logistics, 56, 563, 10.1002/nav.20366
Kharoufeh, 2005, Evaluating failure time probabilities for a Markovian wear process, Computers and Operations Research, 32, 1131, 10.1016/j.cor.2003.09.016
Kharoufeh, 2010, Semi-Markov models for degradation-based reliability, IIE Transactions, 42, 599, 10.1080/07408170903394371
Kim, 2009, Optimal burn-in for maximizing reliability of repairable non-series systems, European Journal of Operational Research, 193, 140, 10.1016/j.ejor.2007.10.037
Kothamasu, 2006, System health monitoring and prognostics – A review of current paradigms and practices, International Journal of Advanced Manufacture Technology, 28, 1012, 10.1007/s00170-004-2131-6
Kumar, 1994, Proportional hazards model: A review, Reliability Engineering and System Safety, 44, 177, 10.1016/0951-8320(94)90010-8
Kumar, 1997, Maintenance scheduling under age replacement policy using proportional hazards model and TTT-plotting, European Journal of Operational Research, 99, 507, 10.1016/S0377-2217(96)00317-7
Kuniewslci, 2009, Sampling inspection for the evaluation of time-dependent reliability of deteriorating systems under imperfect defect detection, Reliability Engineering and System Safety, 94, 1480, 10.1016/j.ress.2008.11.013
Lawless, 2002
Lawless, 2004, Covariates and random effects in a Gamma process model with application to degradation and failure, Lifetime Data Analysis, 10, 213, 10.1023/B:LIDA.0000036389.14073.dd
Lee, 2007, A modified EM-algorithm for estimating the parameters of inverse Gaussian distribution based on time-censored Wiener degradation data, Statistica Sinica, 17, 873
Lee, 2006, Threshold regression for survival analysis: Modeling event times by a stochastic process reaching a boundary, Statistical Science, 21, 501, 10.1214/088342306000000330
Lee, 2000, A model for markers and latent health status, Journal of the Royal Statistical Society, Series B (Methodology), 62, 747, 10.1111/1467-9868.00261
Lee, 2006, Intelligent prognostics and e-maintenance, Computers in Industry, 57, 476, 10.1016/j.compind.2006.02.014
Lee, 2009, A case–control study relating railroad worker mortality to diesel exhaust exposure using a threshold regression model, Journal of Statistical Planning and Inference, 139, 1633, 10.1016/j.jspi.2008.05.023
Lee, 2010, Threshold regression for survival data with time-varying covariates, Statistics in Medicine, 29, 896, 10.1002/sim.3808
Leemis, 1987, Variate generation for accelerated life and proportional hazards models, Operations Research, 35, 892, 10.1287/opre.35.6.892
Li, 2009, Gas turbine performance prognostic for condition-based maintenance, Applied Energy, 86, 2152, 10.1016/j.apenergy.2009.02.011
Liao, 2006, Reliability inference for fields conditions from accelerated degradation testing, Naval Research Logistics, 53, 576, 10.1002/nav.20163
Liao, 2006, Optimal design for step-stress accelerated degradation tests, IEEE Transactions on Reliability, 55, 59, 10.1109/TR.2005.863811
Liao, H., Zhao, W., Guo, H., 2006. Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model. In: Reliability and Maintainability Symposium, RAMS’06, Annual, pp. 127–132.
Li, 2000, Stochastic prognostics for rolling element bearings, Mechanical Systems and Signal Processing, 14, 747, 10.1006/mssp.2000.1301
Li, 2010, Reconstruction based fault prognosis for continuous processes, Control Engineering Practice, 18, 1211, 10.1016/j.conengprac.2010.05.012
Lin, D., Makis, V., 2002. State and model parameter estimation for transmissions on heavy hauler trucks using oil data. In: Proceedings of COMADEM 2002, Birmingham, UK, 2–4 September 2002, pp. 339–348.
Lin, 2003, Recursive filters for a partially observable system subject to random failure, Advance in Applied Probability, 35, 207, 10.1239/aap/1046366106
Lin, 2006, Using principal components in a proportional hazards model with applications in condition-based maintenance, Journal of the Operational Research Society, 57, 910, 10.1057/palgrave.jors.2602058
Love, 1991, Application of Weibull proportional hazards modeling to bad-as-old failure data, Quality and Reliability Engineering International, 7, 149, 10.1002/qre.4680070306
Lu, 1993, Using degradation measures to estimate a time-to-failure distribution, Technometrics, 35, 543, 10.2307/1269661
Lu, 1997, Statistical inference of a time-to-failure distribution derived from linear degradation data, Technometrics, 39, 391, 10.2307/1271503
Lugtigheid, 2008, A finite horizon model for repairable systems with repair restrictions, Journal of the Operational Research Society, 59, 1321, 10.1057/palgrave.jors.2602471
Luo, 2008, Model-based prognostic techniques applied to a suspension system, IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 38, 1156, 10.1109/TSMCA.2008.2001055
Makis, 1991, Computation of optimal policies in replacement models, IMA Journal of Mathematics Applied in Business and Industry, 3, 169
Makis, 1992, Optimal replacement policy for a general model with imperfect repair, Journal of the Operational Research Society, 43, 111, 10.1057/jors.1992.17
Mazhar, 2007, Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks, Journal of Operations Management, 25, 1184, 10.1016/j.jom.2007.01.021
Meeker, 1998
Mehr, 1965, Certain property of Gaussian processes and their first-passage times, Journal of the Royal Statistical Society, Series B (Methodology), 27, 505
Myötyri, 2006, Application of stochastic filtering for lifetime prediction, Reliability Engineering and Systems Safety, 91, 200, 10.1016/j.ress.2005.01.002
Nardo, 2001, A computational approach to first-passage-time problems for Gauss–Markov processes, Advanced Applied Probability, 33, 453, 10.1239/aap/999188324
Navarro, 2010, Comparisons and bounds for expected lifetimes of reliability systems, European Journal of Operational Research, 207, 309, 10.1016/j.ejor.2010.05.001
Nicolai, 2009, Modelling and optimizing imperfect maintenance of coatings on steel structures, Structural Safety, 37, 234, 10.1016/j.strusafe.2008.06.015
Ocak, 2007, Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics, Journal of Sound and Vibration, 302, 951, 10.1016/j.jsv.2007.01.001
Orchard, 2009, A particle-filtering approach for on-line fault diagnosis and failure prognosis, Transactions of the Institute of Measurement and Control, 31, 221, 10.1177/0142331208092026
Padgett, 2004, Inference from accelerated degradation and failure data based on Gaussian models, Lifetime Data Analysis, 10, 191, 10.1023/B:LIDA.0000030203.49001.b6
Pandey, 2009, The influence of temporal uncertainty of deterioration on life-cycle management of structures, Structure and Infrastructure Engineering, 5, 145, 10.1080/15732470601012154
Papakostas, 2010, An approach to operational aircraft maintenance planning, Decision Support Systems, 48, 604, 10.1016/j.dss.2009.11.010
Park, 2010, Direct prediction methods on lifetime distribution of organic light-emitting diodes from accelerated degradation test, IEEE Transaction on Reliability, 59, 74, 10.1109/TR.2010.2040761
Park, 2005, New cumulative damage models for failure using stochastic processes as initial damage, IEEE Transactions on Reliability, 54, 530, 10.1109/TR.2005.853278
Park, 2005, Accelerated degradation models for failure based on geometric Brownian motion and Gamma processes, Lifetime Data Analysis, 11, 511, 10.1007/s10985-005-5237-8
Park, 2006, Stochastic degradation models with several accelerating variables, IEEE Transactions on Reliability, 55, 379, 10.1109/TR.2006.874937
Pecht, 2008
Pecht, 2010, A prognostics and health management roadmap for information and electronics-rich system, Microelectronics Reliability, 50, 317, 10.1016/j.microrel.2010.01.006
Peng, 2011, A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction, Mechanical Systems and Signal Processing, 25, 237, 10.1016/j.ymssp.2010.04.002
Peng, 2009, Mis-specification analysis of linear degradation models, IEEE Transactions on Reliability, 58, 444, 10.1109/TR.2009.2026784
Peng, 2010, Current status of machine prognostics in condition-based maintenance: a review, International Journal of Advanced Manufacture Technology, 50, 297, 10.1007/s00170-009-2482-0
Pennell, 2010, Bayesian random-effects threshold regression with application to survival data with nonproportional hazards, Biostatistics, 11, 111, 10.1093/biostatistics/kxp041
Percy, 2002, Bayesian enhanced strategic decision making for reliability, European Journal of Operational Research, 139, 133, 10.1016/S0377-2217(01)00177-1
Percy, 2007, Scheduling preventive maintenance for oil pumps using generalized proportional intensities models, International Transactions in Operational Research, 14, 547, 10.1111/j.1475-3995.2007.00613.x
Phelps, 2007, Prediction time to failure using the IMM and excitable test, IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 37, 630, 10.1109/TSMCA.2007.902621
Qiu, 2006, Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of Sound and Vibration, 289, 1006, 10.1016/j.jsv.2005.03.007
Rabiner, 1989, A tutorial on hidden Markov models and selected application in speech recognition, Proceedings of the IEEE, 77, 257, 10.1109/5.18626
Ray, 1999, Stochastic modeling of fatigue crack damage for information-based maintenance, Annals of Operations Research, 91, 191, 10.1023/A:1018993505714
Ray, 1996, Stochastic modeling of fatigue crack dynamics for on-line failure prognostics, IEEE Transactions on Control Systems Technology, 4, 443, 10.1109/87.508893
Reinertsen, 1996, Residual life of technical systems; diagnosis, prediction and life extension, Reliability Engineering and System Safety, 54, 23, 10.1016/S0951-8320(96)00092-0
Robinson, 2000, Bayesian methods for a growth-curve degradation model with repeated measures, Lifetime Data Analysis, 6, 357, 10.1023/A:1026509432144
Saha, 2009, Comparison of prognostic algorithms for estimating remaining useful life of batteries, Transactions of the Institute of Measurement and Control, 31, 293, 10.1177/0142331208092030
Sarma, 1978, A decision theory model for health monitoring of aeroengines, Journal of Aircraft, 16, 222, 10.2514/3.58508
Scarf, 1997, On the application of mathematical models in maintenance, European Journal of Operational Research, 99, 493, 10.1016/S0377-2217(96)00316-5
Silver, 1995, Preventive maintenance with limited historical data, European Journal of Operational Research, 82, 125, 10.1016/0377-2217(93)E0315-O
Singpurwalla, 1995, Survival in dynamic environments, Statistical Science, 10, 86, 10.1214/ss/1177010132
Singpurwalla, N.D., 2006. On competing risk and degradation process. Lecture Notes-Monograph Series, 2nd Lehmann Symposium-Optimality, vol. 49, pp. 229–240.
Singpurwalla, 1998, Failure models indexed by two scales, Advances in Applied Probability, 30, 1058, 10.1239/aap/1035228207
Sun, 2006, Mechanical systems hazard estimation using condition monitoring, Mechanical Systems and Signal Processing, 20, 1189, 10.1016/j.ymssp.2004.10.009
Tai, 2009, Detection of machine failure: Hidden Markov model approach, Computer & Industrial Engineering, 57, 608, 10.1016/j.cie.2008.09.028
Tang, 2008, Estimating failure time distribution and its parameters based on intermediate data from a Wiener degradation model, Naval Research Logistics, 55, 265, 10.1002/nav.20280
Tang, L., DeCastro, J., Kacprzynski, G., Goebel, K., Vachtsevanos, G., 2010. Filtering and prediction techniques for model-based prognosis and uncertainty management. In: Prognostics and System Health Management Conference, Macau.
Tseng, 2004, Optimal burn-in policy by using an integrated Wiener process, IIE Transactions, 36, 1161, 10.1080/07408170490507701
Tseng, 2007, Stochastic diffusion modeling of degradation data, Journal of Data Science, 5, 315, 10.6339/JDS.2007.05(3).351
Tseng, 1995, Using degradation data to improve fluorescent lamp reliability, Journal of Quality Technology, 27, 363, 10.1080/00224065.1995.11979618
Tseng, 2003, Determination of optimal burn-in parameters and residual life for highly reliable products, Naval Research Logistics, 50, 1, 10.1002/nav.10042
Tseng, 2009, Optimal step-stress accelerated degradation test plan for Gamma degradation processes, IEEE Transactions on Reliability, 58, 611, 10.1109/TR.2009.2033734
Tweedie, 1957, Statistical properties of inverse Gaussian distributions I, The Annals of Mathematical Statistics, 28, 362, 10.1214/aoms/1177706964
Upadhyaya, 1994, Residual life estimation of plant components, P/PM Technology, 7, 22
Usynin, A., Hines, J.W., 2007. Use of linear growth models for remaining useful life prediction. In: The Maintenance and Reliability Conference (MARCON 2007), May 8–11, Knoxville, Tennessee, USA. <http://www.marcon-2007.com/>.
van Noortwijk, 2009, A survey of the application of gamma processes in maintenance, Reliability Engineering and System Safety, 94, 2, 10.1016/j.ress.2007.03.019
van Noortwijk, 1992, Expert judgment in maintenance optimization, IEEE Transactions on Reliability, 41, 427, 10.1109/24.159813
van Noortwijk, 2007, Gamma processes and peaks-over-threshold distributions for time-dependent reliability, Reliability Engineering and System Safety, 92, 1651, 10.1016/j.ress.2006.11.003
Vlok, 2002, Optimal component replacement decisions using vibration monitoring and the proportional-hazards model, Journal of the Operational Research Society, 53, 193, 10.1057/palgrave.jors.2601261
Vlok, 2004, Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions, Mechanical Systems and Signal Processing, 18, 833, 10.1016/j.ymssp.2003.09.003
Wang, 1997, Subjective estimation of the delay time distribution in maintenance modelling, European Journal of Operational Research, 99, 516, 10.1016/S0377-2217(96)00318-9
Wang, 2000, A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance, International Journal of Production Research, 38, 1425, 10.1080/002075400188933
Wang, 2002, A survey of maintenance policies of deteriorating systems, European Journal of Operational Research, 139, 469, 10.1016/S0377-2217(01)00197-7
Wang, 2002, A model to predict the residual life of rolling element bearings given monitored condition information to date, IMA Journal of Management Mathematics, 13, 3, 10.1093/imaman/13.1.3
Wang, 2003, Modelling condition monitoring intervals: A hybrid of simulation and analytical approaches, Journal of the Operational Research Society, 54, 273, 10.1057/palgrave.jors.2601508
Wang, 2006, Modelling the probability assessment of system state prognosis using available condition monitoring information, IMA Journal of Management Mathematics, 17, 225, 10.1093/imaman/dpi035
Wang, 2007, A two-stage prognosis model in condition based maintenance, European Journal of Operational Research, 182, 1177, 10.1016/j.ejor.2006.08.047
Wang, 2007, A prognosis model for wear prediction based oil-based monitoring, Journal of the Operational Research Society, 58, 887, 10.1057/palgrave.jors.2602185
Wang, 2010, Wiener processes with random effects for degradation data, Journal of Multivariate Analysis, 101, 340, 10.1016/j.jmva.2008.12.007
Wang, W., Carr, M., 2010. An adapted Brownian motion model for plant residual life prediction. In: 2010 Prognostics and System Health Management Conference, Macau.
Wang, 2000, Towards a general condition based maintenance model for a stochastic dynamic system, Journal of the Operational Research Society, 51, 145, 10.1057/palgrave.jors.2600863
Wang, 2009, Plant residual time modelling based on observed variables in oil samples, Journal of the Operational Research Society, 60, 789, 10.1057/palgrave.jors.2602621
Wang, 2005, A model to predict the residual life of aircraft engines based upon oil analysis data, Naval Research Logistics, 52, 276, 10.1002/nav.20072
Wang, 2008, An asset residual life prediction model based on expert judgments, European Journal of Operational Research, 188, 496, 10.1016/j.ejor.2007.03.044
Wang, W., Scarf, P., Sharp, J.M., 1997. Modelling condition based maintenance of production plant. In: Jantunen, Erkki (Ed.), Proceedings of the COMADEM 97, 9–11 June, Espoo, Finland. Julkaisia-Utgivare-Publisher, Espoo, pp. 75–84.
Wang, 2000, On the application of a model of condition-based maintenance, Journal of the Operational Research Society, 51, 1218, 10.1057/palgrave.jors.2601042
Wang, 2009, A condition-based replacement and spare provisioning policy for deteriorating systems with uncertain deterioration to failure, European Journal of Operational Research, 194, 184, 10.1016/j.ejor.2007.12.012
Whitmore, 1986, Normal-Gamma mixture of inverse Gaussian distributions, Scandinavian Journal of Statistics, 13, 211
Whitmore, 1995, Estimating degradation by a Wiener diffusion process subject to measurement error, Lifetime Data Analysis, 1, 307, 10.1007/BF00985762
Whitmore, 1997, Modelling accelerated degradation data using Wiener diffusion with a time scale transformation, Lifetime Data Analysis, 3, 27, 10.1023/A:1009664101413
Whitmore, 2007, Modeling low birth weights using threshold regression, Lifetime Data Analysis, 13, 161, 10.1007/s10985-006-9032-y
Whitmore, 1998, Failure inference from a marker process based on a bivariate Wiener model, Lifetime Data Analysis, 4, 229, 10.1023/A:1009617814586
Xu, 2008, Real-time reliability prediction for a dynamic system based on the hidden degradation process identification, IEEE Transactions on Reliability, 57, 230, 10.1109/TR.2008.916882
You, 2010, Two-zone proportional hazard model for equipment remaining useful life prediction, Journal of Manufacturing Science and Engineering, 132, 10.1115/1.4001580
Yu, 2009, Hidden semi-Markov models, Artificial Intelligence, 174, 215, 10.1016/j.artint.2009.11.011
Zhao, 2010, Condition-based inspection/replacement policies for non-monotone deteriorating systems with environmental covariates, Reliability Engineering and System Safety, 95, 921, 10.1016/j.ress.2010.04.005
Zhou, 2010, A model for real-time failure prognosis based on hidden Markov model and belief rule base, European Journal of Operational Research, 207, 269, 10.1016/j.ejor.2010.03.032
Zio, 2009, Reliability engineering: Old problems and new challenges, Reliability Engineering and System Safety, 94, 125, 10.1016/j.ress.2008.06.002
Zuashkiani, 2009, Estimating parameters of proportional hazards model based on expert knowledge and statistical data, Journal of the Operational Research Society, 60, 1621, 10.1057/jors.2008.119