Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri

Demet ÖZGÜR ÜNLÜAKIN (IŞIK ÜNİVERSİTESİ)
Sezgin ÇAĞLAR AKSEZER (IŞIK ÜNİVERSİTESİ)
1 1

Proje Grubu: MAG Sayfa Sayısı: 189 Proje No: 117M587 Proje Bitiş Tarihi: 15.02.2020 Metin Dili: Türkçe İndeks Tarihi: 23-03-2021

Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri

Öz:
Endüstrinin gelismesiyle sistemlerin karmasıklıgı ve buna baglı olarak da isletmelerin bakım giderleri artmıstır. Bunun sonucunda günümüzde bakım faaliyetlerinin etkin planlanması ve yönetilmesi büyük önem kazanmıstır. Arıza ya da hata yüzünden gerçeklesen plansız makine durusları hemen hemen her sektörde çok ciddi sonuçlar dogurabilmektedir. Bu projenin amacı bilesenleri arasında çesitli bagımlılıkları olan kısmen gözlemlenebilir karmasık çok-bilesenli sistemlerin bakım eniyilemesinde olasılıklı grafiksel modellerin kullanımını kesfetmek, bu tarz problemleri temsil eden bir gerçek-hayat sistemi üstünden konuyu ele alıp bu sistem için DBN ve POMDP modelleri gelistirmek ve bu modeller yardımıyla etkin bakım politikaları olusturmaktır. Elektrik santralleri, birbirleriyle etkilesimli bilesenlerden olusan karmasık sistemlere sahiptir. Beklenmeyen bir arıza çok ciddi maliyetlere neden olacagından dolayı bakım eniyilemesi, bu sektör için çok kritiktir. Elektrik santrallerindeki çok-bilesenli sistemlerin bakım eniyilemesi, bildigimiz kadarıyla, daha önceden çalısılmamıstır. Çok-bilesenli sistemlerin bakım eniyileme problemi de, henuz literatürde az çalısılmıs olup tek-bilesenli sistemlerin bakım eniyilemesinden daha zordur. Bu çalısmada termik santrallerdeki çok-bilesenli kritik sistemlerden biri olan dönerli hava ısıtıcısı, Luvo sistemi, için DBN ve POMDP'leri kullanarak degisik bakım stratejileriyle politikalar elde edilmistir. DBN'ler karmasık sistemsel iliskilerin ve zamansal degisimlerin modellenmesi ile olasılık çıkarımları konularında çok basarılı olup eniyileme yapmazlar. Ancak DBN bazlı gelistirilen sezgiseller eniyileme problemlerinde kullanılabilirler. Diger yandan POMDP'ler sıralı karar problemlerinde çok basarılı olup eniyileme yaparlar. Ancak POMDP'lerin ``boyutluluk'' ve ``geçmis'' problemleri oldugundan küçük boyutlu problemler dısında en iyi sonucu bulmakta zorlanırlar. Böyle durumlarda, yaklasık algoritmaları kullanarak yaklasık politikalar elde edilse de ele aldıkları problemlerdeki iliskiler karmasıklastıkça ve degisken ile durum sayıları arttıkça bu politikaların çözüm kaliteleri düsmektedir. Proje kapsamında olusturulan farklı zorluk seviyesinde iki varyantlı Luvo bakım problemi ve deneysel baska problemler üzerinden DBN ve POMDP bazlı stratejiler kapsamlı senaryolar kurgulanarak bu noktalar ısıgında ayrıntılı olarak analiz edilip karsılastırılmıstır.
Anahtar Kelime: elektrik santralleri dbn pomdp bakım eniyilemesi

Konular: Endüstri Mühendisliği
Erişim Türü: Erişime Açık
  • [1] AlDurgam, M. and Duffuaa, S. 2013. “Optimal joint maintenance and operation policies to maximise overall systems effectiveness”, International Journal of Production Research, 51(5), 1319-1330.
  • 1- A DBN based reactive maintenance model for a complex system in thermal power plants (Makale - Indeksli Makale),
  • [2] Amin, M. T., Khan F. and Imtiaz S. 2019. “Fault detection and pathway analysis using a dynamic Bayesian network”, Chemical Engineering Science, 195, 777–790.
  • 2- Analysis Of Reactive Maintenance Strategies On A Multi-component System Using Dynamic Bayesian Networks (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [3] Andrews, J.D. and Dunnett, S. J. 2000. “Event-tree analysis using binary decision diagrams”, IEEE Transactions on Reliability, 49(2), 230–238.
  • 3- An Effective Maintenance Policy for a Multi-Component Dynamic System using Factored POMDPs (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [4] Araya-López M., Thomas V., Buffet O. and Charpillet F. 2010. “A closer look at MOMDPs”, In 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2, 197–204.
  • 4- Maintenance Policy Simulation for a Factored Partially Observable System (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [5] Aström, K. J. 1965. “Optimal control of Markov decision processes with incomplete state estimation”, J. Math. Anal. Appl., 10, 174–205.
  • 5- Karmasık Sistemlerin Bakım Kararlarında Olasılıklı Grafiksel Modellerin Kullanımı (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [6] Aviv, Y. and Pazgal, A. 2005. “A partially observed Markov decision process for dynamic pricing”, Management Science, 51(9), 1400–1416.
  • 6- Maintenance Optimization for a Multi Component System Using Factored Partially Observable Markov Decision Processes (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [7] Bai, H., Hsu, D., Lee, W. S, and Ngo, V. A. 2010. “Monte carlo value iteration for continuousstate POMDPs”, In Algorithmic foundations of robotics IX, Berlin, Heidelberg: Springer, 175– 191.
  • 7- Comparison of Various Policies under Different Maintenance Strategies on a Multi- Component System (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [8] Barros, A., Berenguer, C. and Grall, A. 2006. “A maintenance policy for two-unit parallel systems based on imperfect monitoring information”, Reliability Engineering & System Safety, 91(2), 131–136.
  • 8- Analyzing the Performance of Different Cost-Based Methods for the Corrective Maintenance of a System in Thermal Power Plants (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [9] Barto, A. G., Bradtke, S. J. and Singh, S. P. 1995. “Learning to act using real-time dynamic programming”, Artificial Intelligence, 72(1-2), 81–138.
  • 9- A DBN Based Prognosis Model For A Complex Dynamic System: A Case Study In A Thermal Power Plant (Bildiri - Uluslararası Bildiri - Sözlü Sunum),
  • [10] Barua, S., Gao, X., Pasman, H. and Mannan, M. S. 2016. “Bayesian network based dynamic operational risk assessment”, Journal of Loss Prevention in the Process Industries, 41, 399– 410.
  • 10- Çok-Bilesenli Bir Sistem Üzerinde Dinamik Bayesçi Aglar Kullanarak Farklı Bakım Politikalarının Analizi (Bildiri - Ulusal Bildiri - Sözlü Sunum),
  • [11] BayesFusion. “GeNIe modeler”. https://www.bayesfusion.com/genie-modeler/. Son Eri¸sim Tarihi: 1 Temmuz 2020. Proje Sonuç Raporu 153
  • 11- Karmasık Sistemlerin Dinamik Bayesçi Aglar ile Bakım Modellemesi: Bir Vaka Çalısması (Bildiri - Ulusal Bildiri - Sözlü Sunum),
  • [12] Bellman, R. 1957. “A Markovian decision process”, Journal of Mathematics and Mechanics, 679–684.
  • 12- Analysis of Different Maintenance Policies on a Multi-component System using Dynamic Bayesian Networks (Tez (Arastırmacı Yetistirilmesi) - Yüksek Lisans Tezi),
  • [13] Bellman, R. 1966. “Dynamic programming”, Science, 153(3731), 34–37.
  • 13- Maintenance Optimization Of Multiple Component Systems Using Probabilistic Graphical Models (Tez (Arastırmacı Yetistirilmesi) - Yüksek Lisans Tezi),
  • [14] Bellman, R. 1954. “The theory of dynamic programming”, Bulletin of the American Mathematical Society, 60(6), 503–515.
  • [15] Bernstein, D. S. 2005. “Complexity analysis and optimal algorithms for decentralized decision making”, PhD thesis, Department of Computer Science, University of Massachusetts Amherst, Amherst, MA.
  • [16] Boger, J., Poupart, P., Hoey, J., Boutilier, C., Fernie, G., and Mihailidis, A. 2005. “A decisiontheoretic approach to task assistance for persons with dementia”, In Proceedings of the IEEE International Joint Conference on Artificial Intelligence, Edinburgh, 1293–1299.
  • [17] Bonet, B. 2002. “An epsilon-optimal grid-based algorithm for partially observable Markov decision processes”, In Proceedings of the Nineteenth International Conference on Machine Learning, San Francisco, CA, USA, 51–58.
  • [18] Bonet, B., Geffner, H. and Labeled R.T.D.P. 2003. “Improving the convergence of real-time dynamic programming”, In International Conference on Automated Planning and Scheduling, Trento, Italy, 3, 12–21.
  • [19] Boutilier, C. and Poole, D. 1996. “Computing optimal policies for partially observable decision processes using compact representations”, In Proceedings of the Thirteenth National Conference on Artificial Intelligence, 2, 1168–1175.
  • [20] Bouvard, K., Artus, S., Bérenguer, C., and Cocquempot, V. 2011. “Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles”, Reliability Engineering & System Safety, 96(6), 601–610.
  • [21] International Civil Aviation Organization (ICAO). “BowtieXP Methodology Manual v15”. https://www.icao.int/safety/SafetyManagement/SMI/Documents/Forms/AllItems.aspx, Son eri¸sim tarihi: 1 Temmuz 2020.
  • [22] Brafman, R. I. 1997. “A heuristic variable grid solution method for POMDPs”, In Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Conference on Innovative Applications of Artificial Intelligence, 727–733. Proje Sonuç Raporu 154
  • [23] Braziunas, D. and Boutilier, C. 2004. “Stochastic local search for POMDP controllers”, In In Proc. of the National Conference on Artificial Intelligence, San Jose, CA, 690–696.
  • [24] Bryant, R. E. 1992. “Symbolic boolean manipulation with ordered binary-decision diagrams”, ACM Computing Surveys (CSUR), 24(3), 293–318.
  • [25] Budai, G., Huisman, D. and Dekker, R. 2006. “Scheduling preventive railway maintenance activities”, Journal of the Operational Research Society, 57(9), 1035–1044.
  • [26] Bui, T. H., Zwiers, J., Poel, M. and Nijholt, A. 2010. “Affective dialogue management using factored POMDPs”, Interactive Collaborative Information Systems, 281, 207–236.
  • [27] Byon, E., Ntaimo, L. and Ding, Y. 2010. “Optimal maintenance strategies for wind turbine systems under stochastic weather conditions”, IEEE Transactions on Reliability, 59(2), 393– 404.
  • [28] Capitán, J., Merino L. and Ollero, A.2011. “Multi-robot coordinated decision making under mixed observability through decentralized data fusion”, In Proceedings of the 11th International Conference on Mobile Robots and Competitions.
  • [29] The POMDP Page. “Pomdp-solve [software, version 5.4]”. https://www.pomdp.org/code/, Son eri¸sim tarihi: 1 Temmuz 2020.
  • [30] Cassandra, A. R., Kaelbling, L. P., and Littman, M. 1994. “Acting optimally in partially observable stochastic domains”, Technical report, Providence, RI, USA.
  • [31] Cassandra, A. R., Kaelbling, L. P., and Kurien, J. A. 1996. “Acting under uncertainty: Discrete Bayesian models for mobile robot navigation”, In International Conference on Intelligent Robots and Systems.
  • [32] Cassandra, A. R., Littman, M. and Zhang N. 1997. “Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes”, In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence, 54–61.
  • [33] Castanier, B., Bérenguer, C. and Grall, A. 2003. “A sequential condition-based repair/replacement policy with non-periodic inspections for a system subject to continuous wear”, Applied Stochastic Models in Business and Industry, 19(4), 327–347.
  • [34] Chan, K. T., Lee, R. and Burnett, J. 2001. “Maintenance performance: a case study of hospitality engineering systems”, Facilities, 19(13/14), 494–504. Proje Sonuç Raporu 155
  • [35] Chang, Y., Wu, X., Zhang, C., Chen, G., Liu, X., Li, J., Cai, B. and Xu, L. 2019. “Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life”, Reliability Engineering & System Safety, 188, 454–462.
  • [36] Chen J., Zhong P. A., An, R., Zhu, F. and Xu, B. 2019. “Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network”, Environmental modelling & software, 111:409–420.
  • [37] Cheng, H. T. 1988. “Algorithms for partially observable Markov decision processes”, PhD thesis, University of British Columbia.
  • [38] Codetta-Raiteri, D., Bobbio A., Montani, S. and Portinale, L. 2012. “A dynamic Bayesian network based framework to evaluate cascading effects in a power grid”, Engineering Applications of Artificial Intelligence, 25(4), 683–697.
  • [39] Codetta-Raiteri, D. and Portinale, L. 2014. “Dynamic Bayesian networks for fault detection, identification, and recovery in autonomous spacecraft”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1), 13–24, 2014.
  • [40] Corotis, R. B., Ellis, J. H. and Jiang, M. 2005. “Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision processes”, Structure and Infrastructure Engineering, 1(1), 75–84.
  • [41] Dabrowski, J. J., Beyers, C. and Villiers, J. P. 2016. “Systemic banking crisis early warning systems using dynamic Bayesian networks”, Expert Systems with Applications, 62, 225–242.
  • [42] Dagum, P. and Galper, A. 1993. “Forecasting sleep apnea with dynamic network models“, In Proc. Ninth Annual Conference on Uncertainty in Artificial Intelligence, 64–71.
  • [43] Dagum, P. and Galper, A. and Horvitz, E. 1992. “Dynamic network models for forecasting“, In Proc. Eighth Annual Conference on Uncertainty in Artificial Intelligence, pages 41–48.
  • [44] Darrell, T. and Pentland, A. 1996. “Active gesture recognition using partially observable Markov decision processes“, In the Proceedings of 13th International Conference on Pattern Recognition, 3, 984–988.
  • [45] David, I., Friedman, L. and Sinuany-Stern, Z. 1999. “A simple suboptimal algorithm for system maintenance under partial observability”, Annals of Operations Research, 91, 25–40.
  • [46] Dean, T., Kaelbling L. P., Kirman, J. and Nicholson, A. 1995. “Planning under time constraints in stochastic domains”, Artificial Intelligence, 76(1-2), 35–74. Proje Sonuç Raporu 156
  • [47] Degris, T. and Sigaud, O. 2013. “Factored Markov decision processes”, Markov Decision Processes in Artificial Intelligence, Editörler: Olivier Sigaud, O., Buffet, O., 99–126.
  • [48] Dekker, R., Plasmeijer, R. P. and Swart, J. H. 1998. “Evaluation of a new maintenance concept for the preservation of highways”, IMA Journal of Management Mathematics, 9(2), 109–156.
  • [49] Dekker, R., Wildeman, R. E. and Duyn Schouten, F. V. 1997. “A review of multi-component maintenance models with economic dependence”, Mathematical Methods of Operations Research, 45(3), 411–435.
  • [50] Delgado, K. V., Sanner, S. and De Barros, L. N. and Cozman F. G. 2011. “Efficient solutions to factored MDPs with imprecise transition probabilities”, Artificial Intelligence, 175(9-10), 1498– 1527.
  • [51] Deodatis, G., Asada, H, and Ito, S. 1996. “Reliability of aircraft structures under non-periodic inspection: a Bayesian approach”, Engineering Fracture Mechanics, 53(5), 789–805.
  • [52] Deodatis, G., Fujimoto, Y., Ito, S., Spencer, J. and Itagaki, H. 1992.“Non-periodic inspection by Bayesian method I”, Probabilistic Engineering Mechanics, 7(4), 191–204.
  • [53] Dibangoye, J., Chaib-draa, B and Mouaddib, A. 2008. “Topological orders based planning for solving POMDPs”, In the Proceedings of Twenty-Third Conference on Artificial Intelligence. [54] Drake, A. 1962. “Observation of a Markov process through a noisy channel”, PhD thesis, Massachusetts Institute of Technology.
  • [55] Du, Y., Hsu, D., Huang, X., Kurniawati, H., Sun Lee, W., Ong, S., and Png, S. “Approximate POMDP planning software”, https://bigbird.comp.nus.edu.sg/pmwiki/farm/appl/index.php?n=Main.HomePage. Son eri¸sim tarihi: 1 Temmuz 2020.
  • [56] Egorov, M., Sunberg, Z. N., Balaban, E., Wheeler, T. A., Gupta, J. K. and Kochenderfer, M. J. 2017. “POMDPs.jl: A framework for sequential decision making under uncertainty”, The Journal of Machine Learning Research, 18(1), 831–835.
  • [57] Eisemann, R. C. 1998. “Machinery malfunction diagnosis and correction: Vibration analysis and troubleshooting for process industries”, Michigan Üniversitesi: Prentice Hall PTR.
  • [58] Ellis, H., Jiang, M. and Corotis, R. B. 1995. “Inspection, maintenance, and repair with partial observability”, Journal of Infrastructure Systems, 1(2), 92–99. Proje Sonuç Raporu 157
  • [59] Emilie M. D., Baptiste, J., Rotshtein, P. and Russell, M. 2015. “POMDP based action planning and human error detection”, In International Conference on Artificial Intelligence Applications and Innovations, 250–265.
  • [60] Engelund, S. and Sørensen, J. D. 1998. “A probabilistic model for chloride-ingress and initiation of corrosion in reinforced concrete structures”, Structural safety, 20(1), 69–89, 1998.
  • [61] Erdogdu, U., Alhajj, R. and Polat, F. 2011. “The benefit of decomposing POMDP for control of gene regulatory networks”, In the Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2, 381–385.
  • [62] Feng, Z. and Hansen, E. A. 2002. “Symbolic heuristic search for factored Markov decision processes”, In the Proceedings of the 18th National Conference on Artificial Intelligence, 455–460.
  • [63] Foka, A. and Trahanias, P. 2007. “Real-time hierarchical POMDPs for autonomous robot navigation”, Robotics and Autonomous Systems, 55(7), 561–571.
  • [64] Games, P. A. and Howell J. F. 1976. “Pairwise multiple comparison procedures with unequal n’s and/or variances: a Monte Carlo study”, Journal of Educational Statistics, 1(2), 113–125.
  • [65] Gendreau, M., and Potvin, J. Y. 2010. Handbook of Metaheuristics (2. Basım). New York: Springer
  • [66] Ghandali, R., Abooie, M. H. and Fallahnezhad, M. S. 2018. “A POMDP framework to find optimal policy in sustainable maintenance”, Scientia Iranica, 2018.
  • [67] Ghosh, M. N and Sharma, D. 1963. “Power of Tukey’s test for non-additivity”, Journal of the Royal Statistical Society. Series B (Methodological), 25(1), 213–219.
  • [68] Goulionis, J.E. and Vozikis, A. 2009. “Medical decision making for patients with Parkinson disease under average cost criterion”, Australia and New Zealand health policy, 6(1), 1–8.
  • [69] Grady, D. K., Moll, D. K. and Kavraki, L. E. 2015. “Extending the applicability of POMDP solutions to robotic tasks”, IEEE Transactions on Robotics, 31(4), 948–961.
  • [70] Grigoriev A., Van De Klundert, J. and Spieksma, F. 2006. “Modeling and solving the periodic maintenance problem”, European Journal of Operational Research, 172(3), 783–797.
  • [71] Gustavsson, E., Patriksson, M., Strömberg, A., Wojciechowski, A. and Önnheim, M. 2014. “Preventive maintenance scheduling of multi-component systems with interval costs”, Computers & Industrial Engineering, 76, 390–400. Proje Sonuç Raporu 158
  • [72] Hansen, E. A. 1998. “An improved policy iteration algorithm for partially observable MDPs”, In Advances in Neural Information Processing Systems, 1015–1021.
  • [73] Hansen, E. A. 1998. “Solving POMDPs by searching in policy space”, In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 211–219.
  • [74] Hansen, E. A. and Feng, Z. 2000. “Dynamic programming for POMDPs using a factored state representation”, In Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling, 130–139.
  • [75] Hauskrecht, M. and Fraser, H. 2000. “Planning treatment of ischemic heart disease with partially‘observable Markov decision processes”, Artificial Intelligence in Medicine, 18(3), 221–244.
  • [76] Heckerman, D., Breese, J. and Rommelse, K. 1994. “Troubleshooting under uncertainty”, Technical Report MSR-TR-94-07, Microsoft Research.
  • [77] Higgins, A. 1998. “Scheduling of railway track maintenance activities and crews”, Journal of the Operational Research Society, 49(10), 1026–1033.
  • [78] Hipkin, T. and De Cock, C. 2000. “TQM and BPR: lessons for maintenance management”, Omega, 28(3), 277–292.
  • [79] Hoey, J. “Symbolic Perseus (In Java)”. https://cs.uwaterloo.ca/ jhoey/research/spudd/index.php. Son Eri¸sim Tarihi: 1 Temmuz 2020.
  • [80] Hoey, J. and Grzes, M. 2011. “Distributed control of situated assistance in large domains with many tasks”, In Twenty-first international conference on automated planning and scheduling.
  • [81] Hoey, J. and Little J. J. 2007. “Value-directed human behavior analysis from video using partially observable Markov decision processes”, IEEE transactions on pattern analysis and machine intelligence, 29(7), 1118–1132.
  • [82] Hoey, J., Poupart P., Bertoldi, A., Craig, T. and Boutilier, C. and Mihailidis, A. 2010. “Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process”, Computer Vision and Image Understanding, 114(5), 503–519.
  • [83] Hoey, J., Poupart P., Bertoldi, A. and Mihailidis, A. 2007. “Assisting persons with dementia during handwashing using a partially observable Markov decision process”, In Proc. Int. Conf. on Vision Systems, 65. Proje Sonuç Raporu 159
  • [84] Hoey, J., Poupart P., Boutilier, C. and Mihailidis, A. 2014. “POMDP models for assistive technology”, Assistive Technologies: Concepts, Methodologies, Tools, and Applications, 120– 140. IGI Global.
  • [85] Hoey, J., Poupart P., St-Aubin, R., Hu, A., and Boutilier, C. 1999. “SPUDD: Stochastic planning using decision diagrams”, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, 279–288.
  • [86] Hong, H. P., Zhou, W., Zhang, S. and Ye, W. 2014. “Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components”, Reliability Engineering & System Safety, 121, 276–288.
  • [87] Howard, R. A. 1960. ”Dynamic Programming and Markov Processes”, MIT Press, Cambridge, MA.
  • [88] Hu, J., Zhang, L., Cai, Z. and Wang, Y. 2015a. “An intelligent fault diagnosis system for process plant using a functional HAZOP and DBN integrated methodology”, Engineering Applications of Artificial Intelligence, 45, 119–135.
  • [89] Hu, J., Zhang, L., Cai, Z., Wang, Y. and Wang, A. 2015b. “Fault propagation behavior study and root cause reasoning with dynamic Bayesian network based framework”, Process Safety and Environmental Protection, 97, 25–36.
  • [90] Hu, J., Zhang, L., and Liang, W. 2012. “Opportunistic predictive maintenance for complex multi-component systems based on DBN-HAZOP model”, Process Safety and Environmental Protection, 90(5), 376–388.
  • [91] Hu, J., Zhang, L., Ma, L. and Wang, A. 2011. “An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm”, Expert Systems with Applications, 38(3), 1431–1446.
  • [92] Hu, J., Zhang, L., Tian, W. and Zhou, S. 2017. “DBN based failure prognosis method considering the response of protective layers for the complex industrial systems”, Engineering Failure Analysis, 79, 504–519.
  • [93] Huynh, K., Castro, I. T., Barros, A. and Bérenguer, C. 2012. “Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks”, European journal of operational research, 218(1), 140–151. Proje Sonuç Raporu 160
  • [94] Ibrahim, R., Kucukyazici, B., Verter, V., Gendreau, M. and Blostein, M. 2016. “Designing personalized treatment: An application to anticoagulation therapy”, Production and Operations Management, 25(5), 902–918.
  • [95] Ito, S., Deodatis, G., Fujimoto, Y., Asada, H. and Shinozuka, M. 1992. “Non-periodic inspection by Bayesian method II: structures with elements subjected to different stress levels”, Probabilistic Engineering Mechanics, 7(4), 205–215, 1992.
  • [96] Ivy, J. and Nembhard, H. 2005. “A modeling approach to maintenance decisions using statistical quality control and optimization”, Quality and Reliability Engineering International, 21(4), 355–366
  • [97] Jakob, M. and Moler, Z. 2013. “Modular framework for simulation modelling of interactionrich transport systems”, In 16th International IEEE Conference on Intelligent Transportation Systems, 2152–2159.
  • [98] Jhang, J. P. and Sheu, S. H. 2000. “Optimal age and block replacement policies for a multicomponent system with failure interaction”, International Journal of Systems Science, 31(5), 593–603.
  • [99] Jiang, M., Corotis, R. B. and Ellis, J. H. 2000. “Optimal life-cycle costing with partial observability”, Journal of infrastructure systems, 6(2), 56–66.
  • [100] Kaelbling, L. P., Littman, M. L. and Cassandra, A. R. 1998. “Planning and acting in partially observable stochastic domains”, Artificial intelligence, 101(1-2), 99–134.
  • [101] Kao, H. Y., Huang, C. H. and Li, H. L. 2005. “Supply chain diagnostics with dynamic Bayesian networks”, Computers & Industrial Engineering, 49(2), 339–347.
  • [102] Karkus, P., Hsu, D. and Lee, W. S. 2017. “Qmdp-net: Deep learning for planning under partial observability”, In Advances in Neural Information Processing Systems, 4694–4704.
  • [103] Keller, W. and Modarres, M. 2005. “A historical overview of probabilistic risk assessment development and its use in the nuclear power industry: a tribute to the late Professor Norman Carl Rasmussen”, Reliability Engineering & System Safety, 89(3), 271–285.
  • [104] Kim, D., Sim, H. S., Kim, K. E., Kim, J., Kim, H. and Sung, J. W. 2008. “Effects of user modeling on POMDP-based dialogue systems”, In 9th Annual Conference of the International Speech Communication Association, 1169–1172. Proje Sonuç Raporu 161
  • [105] Kim, J. W., Choi, G. B. and Lee, J. M. 2018. “A POMDP framework for integrated scheduling of infrastructure maintenance and inspection”, Computers & Chemical Engineering, 112, 239–252.
  • [106] Kıvanç, ˙I. and Özgür-Ünlüakın, D. 2019. “An effective maintenance policy for a multicomponent dynamic system using factored POMDPs”, In European Conference on Symbolic and Quantitative Approaches with Uncertainty, 290–300.
  • [107] Kobbacy, K. A. and Murthy, D. P. 2008. “Complex system maintenance handbook”, Springer Science & Business Media.
  • [108] Koltunova, V. 2013. “Active sensing for partially observable Markov decision processes”, Master’s thesis, University of Waterloo.
  • [109] Kothamasu, R., Huang S. H. and VerDuin, W. H. 2006. “System health monitoring and prognostics–a review of current paradigms and practices”, The International Journal of Advanced Manufacturing Technology, 28(9-10), 1012–1024.
  • [110] Kurniawati, H., Hsu, D. and Lee, W. S. 2008. “SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces”, In Proc. Robotics: Science and Systems.
  • [111] Lai M. T. and Chen, Y. C. 2006. “Optimal periodic replacement policy for a two-unit system with failure rate interaction”, The international journal of advanced manufacturing technology, 29(3-4), 367–371.
  • [112] Langseth, H. and Portinale, L. 2007. “Bayesian networks in reliability”, Reliability Engineering & System Safety, 92(1), 92–108.
  • [113] Li, M. and Liu, K. 2018. “Application of intelligent dynamic Bayesian network with wavelet analysis for probabilistic prediction of storm track intensity index”, Atmosphere, 9(6), 224.
  • [114] Li, Z., Xu T., Gu, J., Dong, Q. and Fu, L. 2018. “Reliability modelling and analysis of a multistate element based on a dynamic Bayesian network”, Royal Society open science, 5(4), 171438.
  • [115] Li, Z., Xu T., Gu, J., Wang, H. and Zhao, J. 2019. “Reliability modeling of redundant systems considering CCF based on DBN”, Arabian Journal for Science and Engineering, 44(3), 2567– 2577. Proje Sonuç Raporu 162
  • [116] Lin, S., Zhang, A. and Feng, D. 2016. “Maintenance decision-making model based on POMDP for traction power supply equipment and its application”, In Prognostics and System Health Management Conference, 1–6.
  • [117] Lin, T. W. and Wang, C. H. 2011. “Minimisation of non-periodic preventive maintenance cost in series-parallel systems”, Defence Science Journal, 61(1), 44–50.
  • [118] Littman, M. L. 1994. “The witness algorithm: Solving partially observable Markov decision processes”, Technical Report CS-94-40, Brown University, Providence, RI, 1994.
  • [119] Littman, M. L., Cassandra, A. R. and Kaelbling, L. P.1995a. “Efficient dynamic-programming updates in partially observable Markov decision processes”, Technical Report CS-95-19, Brown University. Providence. RI.
  • [120] Littman, M. L., Cassandra, A. R. and Kaelbling, L. P. 1995b. “Learning policies for partially observable environments: Scaling up”, In Machine Learning Proceedings, 362–370.
  • [121] Littman, M. L., 1996. “Algorithms for sequential decision making”, Ph.D. Thesis, Brown University Providence, RI.
  • [122] Liu, Z., Liu, Y., Cai, B., Zhang, D. and Zheng, C. 2015. “Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures”, Journal of Loss Prevention in the Process Industries, 38, 58–66.
  • [123] Lovejoy, W. S. 1991. “A survey of algorithmic methods for partially observed Markov decision processes”, Annals of Operations Research, 28(1), 47–65.
  • [124] Lusena, C., Goldsmith, J. and Mundhenk, M. 2001. “Nonapproximability results for partially observable Markov decision processes”, Journal of artificial intelligence research, 14, 83-103.
  • [125] Ma, S., Liu, Q. and Sun, X. 2003. “Research on emergency maintenance technique of equipments [J]”, China Surface Engineering, 3, 7–16.
  • [126] Madanat, S. 1993. “Incorporating inspection decisions in pavement management”, Transportation Research Part B: Methodological, 27(6), 425–438.
  • [127] Madanat, S. and Ben-Akiva, M. 1994. “Optimal inspection and repair policies for infrastructure facilities”, Transportation science, 28(1), 55–62.
  • [128] Madani, O., Hanks, S. and Condon, A. 1999. “On the undecidability of probabilistic planning and infinite-horizon partially observable markov decision problems”, In Proceedings of the Sixteenth National Conference on Artificial Intelligence, Menlo Park, CA, USA, 541–548. Proje Sonuç Raporu 163
  • [129] Maillart, L. S., Ivy, L. S., Ransom, S. and Diehl, K. 2008. “Assessing dynamic breast cancer screening policies”, Operations Research, 56(6), 1411–1427.
  • [130] Makis, V. and Jiang, X.2003. “Optimal replacement under partial observations”, Mathematics of Operations Research, 28(2), 382–394.
  • [131] Marini, S., Trifoglio, E., Barbarini, N., Sambo, F., Di Camillo, B., Malovini, A., Manfrini, M., Cobelli, C. and Bellazzi, R. 2015. “A dynamic Bayesian network model for long-term simulation of clinical complications in type 1 diabetes”, Journal of Biomedical Informatics, 57, 369–376, 2015.
  • [132] Mazidi, P„ Mian, D. and Sanz-Bobi, M. A. 2016. “Simulation model based on reliability and maintenance of a component and their effect on cost”, In China International Conference on Electricity Distribution, 1–5.
  • [133] McCallum, R. A. 1995. “Instance-based utile distinctions for reinforcement learning with hidden state”, In Machine Learning Proceedings, 387–395.
  • [134] Mehta, B.R. and Reddy, Y.J. 2015. “Chapter 18 - System Maintenance”, Industrial Process Automation Systems, Editörler: Mehta, B.R. and Reddy, Y.J., 523 – 545. Butterworth- Heinemann.
  • [135] Memarzadeh, M. and Papakonstantinou, K. 2017. “Optimal maintenance and inspection planning for structural components under mixed and partial observability”, In 12th Int. Conf. on Structural Safety and Reliability, 2280-2289.
  • [136] Memarzadeh, M. and Pozzi, M. 2016. “Integrated inspection scheduling and maintenance planning for infrastructure systems”, Computer-Aided Civil and Infrastructure Engineering, 31(6), 403–415.
  • [137] Memarzadeh, M., Pozzi, M. and Colter, J. 2014. “Optimal planning and learning in uncertain environments for the management of wind farms”, Journal of Computing in Civil Engineering, 29(5), 04014076.
  • [138] Mihailidis, A., Boger, J., Craig, T. and Hoey, J. 2008. “The COACH prompting system to assist older adults with dementia through handwashing: An efficacy study”, BMC geriatrics, 8(1), 28–46.
  • [139] Monahan, G. 1982. “State of the art—a survey of partially observable Markov decision processes: theory, models, and algorithms”, Management Science, 28(1), 1–16. Proje Sonuç Raporu 164
  • [140] Morato, P. G., Mai, Q., Rigo, P. and Nielsen, J. S. 2018. “Point-based POMDP risk based inspection of offshore wind substructures”, In Proceedings of the Sixth International Symposium on Life-cycle Civil Engineering.
  • [141] Moubray, J. 2001. “Reliability-centered maintenance”, New York, Industrial Press. [142] Muller, A., Suhner, M. C. and Iung, B. 2008. “Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system”, Reliability Engineering & System Safety, 93(2), 234–253.
  • [143] Murphy, K. P. 2001. “Bayes net toolbox for matlab”, https://www.cs.utah.edu/ tch/notes/matlab/bnt/docs/bnt?pre?sf.html.
  • [144] Murphy, K. P. and Russell, S. 2002. “Dynamic Bayesian networks: representation, inference and learning”, University of California, Berkeley, CA.
  • [145] Murthy, D. and Nguyen, D. 1985. “Study of a multi-component system with failure interaction”, European Journal of Operational Research, 21(3), 330–338.
  • [146] Nguyen, K. A., Do, P. and Grall, A. 2014. “Condition-based maintenance for multi-component systems using importance measure and predictive information”, International Journal of Systems Science: Operations & Logistics, 1(4), 228–245.
  • [147] Nguyen, K. A., Do, P. and Grall, A. 2015. “Multi-level predictive maintenance for multicomponent systems”, Reliability engineering & system safety, 144, 83–94.
  • [148] Nicol, S., Buffet, O., Iwamura, T. and Chadès, I. 2013. “Adaptive management of migratory birds under sea level rise”, In Twenty-Third International Joint Conference on Artificial Intelligence.
  • [149] Nicolai, R. P. and Dekker, R. 2008. Sayfa 263–286. “Optimal maintenance of multicomponent systems: a review”. Complex system maintenance handbook, Editörler: Kobbacy, K. and Murthy, D. N. London: Springer.
  • [150] Nielsen, J. S. and Sørensen, J. D. 2018. “Computational framework for risk-based planning of inspections, maintenance and condition monitoring using discrete Bayesian networks”, Structure and Infrastructure Engineering, 14(8), 1082–1094.
  • [151] Oliehoek, F. and Spaan, M. 2009. “MADP toolbox 0.2”, Technical report, Technical Report, Informatics Institute, Amsterdam University. Proje Sonuç Raporu 165
  • [152] Özgür-Ünlüakin, D. and Bilgiç, T. 2014. “Replacement policies for a complex system with unobservable components using dynamic Bayesian networks”, International Journal of Computational Intelligence Systems, 7(sup1), 68–83.
  • [153] Özgür-Ünlüakin, D. and Karacaörenli, A. 2018. “Analysis of reactive maintenance strategies on a multi-component system using dynamic Bayesian networks”, In The International Symposium for Production Research, 101–110.
  • [154] Özgür-Ünlüakin, D. and Kivanç, ˙I. 2019. “Maintenance policy simulation for a factored partially observable system”, In Proceedings of the 2019 Summer Simulation Conference.
  • [155] Özgür-Ünlüakin, D. and Kivanç, ˙I., Türkali, B. and Aksezer, Ç. 2018. “A DBN based prognosis model for a complex dynamic system: A case study in a thermal power plant”, In The International Symposium for Production Research, 75–84.
  • [156] Özgür-Ünlüakin, D., Türkali, B., Karacaörenli, A. and Aksezer, Ç. 2019. ”A DBN based reactive maintenance model for a complex system in thermal power plants”, Reliability Engineering & System Safety, 190, 106505.
  • [157] Pajarinen, J., Peltonen, J., Hottinen, A. and Uusitalo, M. A. 2010. “Efficient planning in large POMDPs through policy graph based factorized approximations”, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 1–16.
  • [158] Papadakis, I. and Kleindorfer, P. R. 2005. “Optimizing infrastructure network maintenance when benefits are interdependent”, OR Spectrum, 27(1), 63–84.
  • [159] Papadimitriou, C. and Tsitsiklis, J. N. 1987. “The complexity of Markov decision processes”, Math. Oper. Res., 12(3), 441–450.
  • [160] Papakonstantinou, K. G., Andriotis, C. P. and Shinozuka, M. 2016a. “Point-based POMDP solvers for lifecycle cost minimization of deteriorating structures”, In Proceedings of the 5th International Symposium on Life-Cycle Civil Engineering.
  • [161] Papakonstantinou, K. G., Andriotis, C. P. and Shinozuka, M. 2016b. “POMP solutions for monitored structures”, In Proc. of IFIP WG-7.5 Conference on Reliability and Optimization of Structural Systems.
  • [162] Papakonstantinou, K. G., Andriotis, C. P. and Shinozuka, M. 2018. “POMDP and MOMDP solutions for structural life-cycle cost minimization under partial and mixed observability”, Structure and Infrastructure Engineering, 14(7), 869–882. Proje Sonuç Raporu 166
  • [163] Papakonstantinou, K. G. and Shinozuka, M. 2014a. “Planning structural inspection and maintenance policies via dynamic programming and Markov processes. part I: Theory”, Reliability Engineering & System Safety, 130, 202–213, 2014.
  • [164] Papakonstantinou, K. G. and Shinozuka, M. 2014b. “Planning structural inspection and maintenance policies via dynamic programming and Markov processes. part II: POMDP implementation”, Reliability Engineering & System Safety, 130, 214–224.
  • [165] Papakonstantinou, K. G. and Shinozuka, M. 2014c. “Optimum inspection and maintenance policies for corroded structures using partially observable Markov decision processes and stochastic, physically based models”, Probabilistic Engineering Mechanics, 37, 93–108.
  • [166] Pearl, J. 1985. “Bayesian networks: A model of self-activated memory for evidential reasoning”, In Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA, USA, 15–17.
  • [167] Pearl, J. 2014. “Probabilistic reasoning in intelligent systems: Networks of plausible inference, Elsevier.
  • [168] Pham, H. and Wang, H. 1996. “Imperfect maintenance”, European journal of operational research, 94(3), 425–438.
  • [169] Pineau,J., Gordon, G., Thrun, S. 2003. “Point-based value iteration: An anytime algorithm for POMDPs”, In Proc. Int. Jnt. Conf. on Artificial Intelligence, 3, 477–484.
  • [170] Pineau, J., Gordon, G. and Thrun, S. 2004. “Tractable planning under uncertainty: exploiting structure”, PhD thesis, Carnegie Mellon University, the Robotics Institute.
  • [171] Pineau, J. and Gordon, G. 2007. “POMDP planning for robust robot control”, In Robotics Research, 69–82.
  • [172] Poole, D. and Mackworth, A. K. 2010. “Artificial intelligence - Foundations of computational agents”.
  • [173] Poupart, P. 2005. “Exploiting structure to efficiently solve large scale partially observable Markov decision processes”, PhD thesis, Toronto, Ont., Canada.
  • [174] Poupart, P. “Symbolic Perseus”. https://cs.uwaterloo.ca/ ppoupart/software.html. Son Eri¸sim Tarihi: 1 Temmuz 2020. Proje Sonuç Raporu 167
  • [175] POWER Magazine. “Air Preheater Seal Upgrades Renew Plant Efficiency”. https://www.powermag.com/air-preheater-seal-upgrades-renew-plant-efficiency/, Son eri¸sim tarihi: 1 Temmuz 2020.
  • [176] Prajapati, A., Bechtel, J. and Ganesan, S. 2012. “Condition based maintenance: a survey”, Journal of Quality in Maintenance Engineering, 18(4), 384–400.
  • [177] Price, J.W. H. and Mathew, J. 2000. “The constant-interval replacement model for preventive maintenance”, International Journal of Quality & Reliability Management, 17(8), 822–838.
  • [178] Puterman, M. L. 2014. “Markov decision processes: Discrete stochastic dynamic programming”, John Wiley & Sons.
  • [179] Ram, M. 2019. “Reliability Engineering: Methods and Applications”, CRC Press.
  • [180] Reyes, A., Spaan, M. and Sucar, L. E. 2009. “An intelligent assistant for power plants based on factored MDPs”, In 15th International Conference on Intelligent System Applications to Power Systems, 1–6.
  • [181] Roos, J., Gavin G. and Bonnevay, S. 2017. “A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data”, Transportation Research Procedia, 26, 53–61.
  • [182] Rosenfield, D. 1976. “Markovian deterioration with uncertain information”, Operations Research, 24(1), 141–155.
  • [183] Ross, S. and Chaib-Draa, B. 2007. “AEMS: An anytime online search algorithm for approximate policy refinement in large POMDPs”, In Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2592–2598.
  • [184] Ross, S., Pineau, J., Paquet, S. and Chaib-Draa, B. 2008. “Online planning algorithms for POMDP”, Journal of Artificial Intelligence Research, 32, 663–704.
  • [185] Rout, T. M. Moore, J. L. and McCarthy, M. A. 2014. “Prevent, search or destroy a partially observable model for invasive species management”, Journal of Applied Ecology, 51(3), 804– 813.
  • [186] Roy, N., Gordon, G. and Thrun, S. 2005. “Finding approximate POMDP solutions through belief compression”, Journal of Artificial Intelligence Research, 23, 1–40. Proje Sonuç Raporu 168
  • [187] Rusmevichientong, P. and Van Roy, B. 2001. “An analysis of belief propagation on the turbo decoding graph with gaussian densities”, IEEE Transactions on Information Theory, 47(2), 745–765.
  • [188] Salazar, J. C., Weber, P., Nejjari, F., Sarrate, R. and Theilliol, D. 2017. “System reliability aware model predictive control framework”, Reliability Engineering & System Safety, 167, 663–672.
  • [189] Sandri, M., Berchialla, P., Baldi, I., Gregori, D. and De Blasi, R. A. 2014. “Dynamic Bayesian networks to predict sequences of organ failures in patients admitted to ICU”, Journal of Biomedical Informatics, 48, 106–113.
  • [190] Sasieni, M. W. 1956. “A Markov chain process in industrial replacement”, Journal of the Operational Research Society, 7(4), 148–155.
  • [191] Satow, T. and Osaki, S. 2003. “Optimal replacement policies for a two-unit system with shock damage interaction”, Computers & Mathematics with Applications, 46(7), 1129–1138.
  • [192] Scarf, P. A. and Deara, M. 1998. “On the development and application of maintenance policies for a two-component system with failure dependence”, IMA Journal of Management Mathematics, 9(2), 91–107.
  • [193] Scarf, P. A. and Deara, M. 2003. “Block replacement policies for a two-component system with failure dependence”, Naval Research Logistics (NRL), 50(1), 70–87.
  • [194] Shafiee, M. and Finkelstein, M. 2015. “An optimal age-based group maintenance policy for multi-unit degrading systems”, Reliability Engineering & System Safety, 134, 230–238.
  • [195] Shani, G., Pineau, J. and Kaplow, R. 2013. “A survey of point-based POMDP solvers”, Autonomous Agents and Multi-Agent Systems, 27(1), 1–51.
  • [196] SharFriedmanma, A., Yadava, G. S. and Deshmukh, S. G. 2011. “A literature review and future perspectives on maintenance optimization”, Journal of Quality in Maintenance Engineering, 17(1), 5–25.
  • [197] Sheu, S. H. and Liou, C. T. 1992. “Optimal replacement of a k-out-of-n system subject to shocks”, Microelectronics Reliability, 32(5), 649–655.
  • [198] Sheut, C. and Krajewski, L. J. 1994. “A decision model for corrective maintenance management”, The International Journal of Production Research, 32(6), 1365–1382. Proje Sonuç Raporu 169
  • [199] Sigaud, O. and Buffet, O. 2013. “Markov decision processes in artificial intelligence”, John Wiley & Sons.
  • [200] Silver, D. and Veness, J. 2010. “Monte-carlo planning in large POMDPs”, In Advances in neural information processing systems, 2164–2172.
  • [201] Simmons, R. and Koenig, S. 1995. “Probabilistic robot navigation in partially observable environments”, In IJCAI, 95, 1080–1087.
  • [202] Smallwood, R. D. and Sondik, E. J. 1973. “The optimal control of partially observable Markov decision processes over a finite horizon”, Operations Research, 21(5), 1071–1088.
  • [203] Smith, M. A. J., and Dekker, R. 1997. “Preventive maintenance in a 1-out-of-n system: the uptime, downtime and costs”, European Journal of Operational Research, 565-583.
  • [204] Smith, T. “ZMDP software for POMDP and MDP planning”. GitHub. https://github.com/trey0/zmdp, Son eri¸sim tarihi: 1 Temmuz 2020.
  • [205] Smith, T. and Simmons, R. 2004. “Heuristic search value iteration for POMDPs”, In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, Arlington, Virginia, United States, 520–527.
  • [206] Smith, T. and Simmons, R. 2006. “Focused real-time dynamic programming for MDPs: Squeezing more out of a heuristic”, In AAAI,1227–1232.
  • [207] Smith, T. and Simmons, R. 2005. “Point-based POMDP algorithms: Improved analysis and implementation”, In Proc. of Uncertainty in Artificial Intelligence, 542–547.
  • [208] Somani, A., Ye, N., Hsu, D. and Lee, W. S. 2013. “Despot: Online POMDP planning with regularization”, In Advances in neural information processing systems, 1772–1780.
  • [209] Sondik, E. J. 1978. “The optimal control of partially observable Markov processes over the infinite horizon: Discounted costs”, Oper. Res., 26(2), 282–304.
  • [210] Sondik, E. J. 1971. “The optimal control of partially observable Markov processes”, Technical report, Stanford Univ Calif Stanford Electronics Labs.
  • [211] Spaan, M., Veiga, T. S. and Lima, P. 2010. “Active cooperative perception in network robot systems using POMDPs”, In International Conference on Intelligent Robots and Systems, 4800–4805. Proje Sonuç Raporu 170
  • [212] Spaan, M. and Vlassis, N. 2005. “Perseus: Randomized point-based value iteration for POMDPs”, Journal of Artificial Intelligence Research, 24, 195–220.
  • [213] Sriskandarajah, C., Jardine, A. K. S. and Chan, C. K. 1998. “Maintenance scheduling of rolling stock using a genetic algorithm”, Journal of the Operational Research Society, 49(11), 1130–1145.
  • [214] St-Aubin, R., Hoey, J. and Boutilier, C. 2001. “APRICODD: Approximate policy construction using decision diagrams”, In Advances in Neural Information Processing Systems, 1089– 1095.
  • [215] Taha, T., Miró, J. V. and Dissanayake, G. 2011. “A POMDP framework for modelling human interaction with assistive robots”, In IEEE International Conference on Robotics and Automation, pages 544–549.
  • [216] Tahara, A and Nishida, T. 1975. “Optimal replacement policy for minimal repair model”, Journal of Operations Research Society of Japan, 18(3–4), 113–124.
  • [217] Theocharous, G. and Mahadevan, S. 2002. “Approximate planning with hierarchical partially observable Markov decision process models for robot navigation”, In Proceedings of IEEE International Conference on Robotics and Automation, 2, 1347–1352.
  • [218] Thompson, P. D., Small, E. P., Johnson, M. and Marshall, A. R. 1998. “The pontis bridge management system”, Structural Engineering International, 8(4), 303–308.
  • [219] Tian, Z. and Liao, H. 2011. “Condition based maintenance optimization for multi-component systems using proportional hazards model”, Reliability Engineering & System Safety, 96(5), 581–589.
  • [220] Treharne, A. J. and Sox, C. R. 2002. “Adaptive inventory control for nonstationary demand and partial information”, Management Science, 48(5), 607–624.
  • [221] Tsang, A. 1995. “Condition-based maintenance: tools and decision making”, Journal of quality in maintenance engineering, 1(3), 3–17.
  • [222] Van Horenbeek, A. and Pintelon, L. 2013. “A dynamic predictive maintenance policy for complex multi-component systems”, Reliability Engineering & System Safety, 120, 39–50.
  • [223] Wang, H. 2002. “A survey of maintenance policies of deteriorating systems”, European journal of operational research, 139(3), 469–489. Proje Sonuç Raporu 171
  • [224] Watson, H. A. and Bell Telephone Laboratories. 1961. “Launch control safety study”, Bell Telephone Laboratories, Murray Hill”, Technical report, NJ, Tech. Rep.
  • [225] Weber, P., Medina-Oliva, G., Simon, C. and Iung, B. 2012. “Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas”, Engineering Applications of Artificial Intelligence, 25(4), 671–682.
  • [226] White, C. C. 1976. “Procedures for the solution of a finite-horizon, partially observed, semi- Markov optimization problem”, Operations Research, 24(2), 348–358.
  • [227] White, C. C. 1977. “A Markov quality control process subject to partial observation”, Management Science, 23(8), 843–852.
  • [228] White, C. C. 1991. “A survey of solution techniques for the partially observed Markov decision process”, Annals of Operations Research, 32(1), 215–230.
  • [229] Williams, J. D. Poupart, P. and Young, S. 2005. “Factored partially observable Markov decision processes for dialogue management”, In Proc. IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems, 76–82.
  • [230] Wu, S., Zhang, L., Zheng, W., Liu, Y. and Lundteigen, M. A. 2016. “A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents”, Journal of Natural Gas Science and Engineering, 34, 139–158.
  • [231] Wu, X., Liu H., Zhang, L., Skibniewski, M. J., Deng, Q. and Teng, J. 2015. “A dynamic Bayesian network based approach to safety decision support in tunnel construction”, Reliability Engineering & System Safety, 134, 157–168.
  • [232] Young, S. 2006. “Using pomdps for dialog management”, In IEEE Spoken Language Technology Workshop, 8–13.
  • [233] Zequeira, R. I. and Bérenguer, C. 2005. “On the inspection policy of a two-component parallel system with failure interaction”, Reliability Engineering & System Safety, 88(1), 99– 107.
  • [234] Zhang, J. and Liu, Z. 1996. “Calculating some inverse linear programming problems”, Journal of Computational and Applied Mathematics, 72(2), 261–273.
  • [235] Zhang, N. L. and Liu, W. 1996. “Planning in stochastic domains: Problem characteristics and approximation”, Technical Report HKUST-CS96-31, Hong Kong University of Science and Technology. Proje Sonuç Raporu 172
  • [236] Zhang, Y., Zheng, R., Shen, G. and Chen, B. 2011. “Reliability analysis for cnc machine tool based on failure interaction”, In International Conference on Intelligent Computing and Information Science, 489–496.
  • [237] Zhou, R. and Hansen, E. 2001. “An improved grid-based approximation algorithm for POMDPs”, In Proc. International Joint Conference on Artificial Intelligence, 707–716.
  • [238] Zhu, B. and Frangopol, D. M. 2012. “Risk-based approach for optimum maintenance of bridges under traffic and earthquake loads”, Journal of Structural Engineering, 139(3), 422– 434.
APA ÜNLÜAKIN D, AKSEZER S (2020). Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. , 1 - 189.
Chicago ÜNLÜAKIN Demet ÖZGÜR,AKSEZER Sezgin ÇAĞLAR Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. (2020): 1 - 189.
MLA ÜNLÜAKIN Demet ÖZGÜR,AKSEZER Sezgin ÇAĞLAR Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. , 2020, ss.1 - 189.
AMA ÜNLÜAKIN D,AKSEZER S Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. . 2020; 1 - 189.
Vancouver ÜNLÜAKIN D,AKSEZER S Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. . 2020; 1 - 189.
IEEE ÜNLÜAKIN D,AKSEZER S "Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri." , ss.1 - 189, 2020.
ISNAD ÜNLÜAKIN, Demet ÖZGÜR - AKSEZER, Sezgin ÇAĞLAR. "Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri". (2020), 1-189.
APA ÜNLÜAKIN D, AKSEZER S (2020). Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. , 1 - 189.
Chicago ÜNLÜAKIN Demet ÖZGÜR,AKSEZER Sezgin ÇAĞLAR Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. (2020): 1 - 189.
MLA ÜNLÜAKIN Demet ÖZGÜR,AKSEZER Sezgin ÇAĞLAR Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. , 2020, ss.1 - 189.
AMA ÜNLÜAKIN D,AKSEZER S Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. . 2020; 1 - 189.
Vancouver ÜNLÜAKIN D,AKSEZER S Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri. . 2020; 1 - 189.
IEEE ÜNLÜAKIN D,AKSEZER S "Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri." , ss.1 - 189, 2020.
ISNAD ÜNLÜAKIN, Demet ÖZGÜR - AKSEZER, Sezgin ÇAĞLAR. "Çok-Bile¸senli Sistemlerin Bakım En iyilemesinde Olasılıklı Grafiksel Modellerin Geli¸ stirilmesi ve Bunların Çözümleri". (2020), 1-189.
Sayfa Oluşturulma Tarihi
24-04-2024 1:46

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları