PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Evaluation of the maintenance system readiness using the semi-Markov model taking into account hidden factors

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Modelling the time that the system remains in a given state using classical distributions is not always possible. In many cases, empirical distributions are multimodal due to the influence of external, hidden factors and the selection of the best classical distributions may lead to erroneous results. In the article the method of diagnosis of influence of hidden factors into sojourn time of semi-Markov models was presented. In order to capture hidden factors, the authors proposed to model the distributions of the sojourn time with a mixture of distributions, which is a significant novelty in relation to the studies presented in the literature. Hidden factors directly affect the reliability of technical systems. Detecting the existence of these factors enables more accurate modeling of system readiness. Paying attention to irregularities caused by hidden factors makes it possible to reduce system maintenance costs. Such a system model providescomplete information and enables a reliable assessment of the system readiness and maintenance.
Rocznik
Strony
art. no. 172857
Opis fizyczny
Bibliogr. 77 poz., rys., tab., wykr.
Bibliografia
  • 1. Act of 6 April 1990 on the Police (Journal of Laws 1990 No. 30 item 179)
  • 2. Alawaysheh I, Alsyouf I, Tahboub ZEA, Almahasneh HS. Selecting maintenance practices based on environmental criteria: a comparative analysis of theory and practice in the public transport sector in UAE/DUBAI.International Journal of System Assurance Engineering and Management 2020;11(6):1133-1155, https://doi.org/10.1007/s13198-020-00964-1
  • 3. Anand MP, Bagen B, Rajapakse A. Probabilistic reliability evaluation of distribution systems considering the spatial and temporal distribution of electric vehicles.International Journal of Electrical Power & Energy Systems, 2020117;105609, https://doi.org/10.1016/j.ijepes.2019.105609
  • 4. Borucka A, Kozłowski E, Parczewski R, Antosz K, Gil L, Pieniak D. Supply sequence modelling using hidden Markov models, Applied Sciences. 2022; 13:231, https://doi.org/10.3390/app13010231
  • 5. Borucka A. Three-state Markov model of using transport means.Business Logistics In Modern Management, 2018; 3-19.
  • 6. Brown RG. Smoothing, forecasting and prediction of discrete time series, Courier Corporation. 2004
  • 7. Buchholz P, Dohndorf I, Scheftelowitsch D, Time-Based Maintenance Models Under Uncertainty, Measurement, Modelling and Evaluation of Computing Systems. MMB 2018, Springer 2018; 3-18, DOI:10.1007/978-3-319-74947-1_1
  • 8. Cao Q, Zanni-Merk C, Samet A, Reich C, de Beuvron FDB, Beckmann A, Giannetti C. KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0.Robotics and Computer-Integrated Manufacturing2022;74:102281.
  • 9. Chen B, Liu Y, Zhang C, Wang Z. Time series data for equipment reliability analysis withdeep learning.IEEE Access,2020;8:105484-105493, https://doi.org/10.1109/ACCESS.2020.3000006
  • 10. Chen C, Liu Y, Sun X, Wang S, Di Cairano-Gilfedder C, Titmus S, Syntetos AA. Reliability analysis using deep learning. InInternational Design Engineering Technical Conferences and Computers and Information in Engineering Conference2018;51739: V01BT02A040. American Society of Mechanical Engineers, https://doi.org/10.1115/DETC2018-86172
  • 11. Chen C, Wang C, Lu N, Jiang B, Xing Y. A data-driven predictive maintenance strategy based on accurate failure prognostics. Eksploatacja i Niezawodność – Maintenance and Reliability. 2021;23(2):387-394. https://doi.org/10.17531/ein.2021.2.19
  • 12. Chen D, Trivedi KS, Optimization for condition-based maintenance with semi-Markov decision process, Reliability Engineering and System Safety 2005;90(1):25-29; DOI:10.1016/j.ress.2004.11.001
  • 13. Dehghani NL, Darestani YM, Shafieezadeh A. Optimal life-cycle resilience enhancement of aging power distribution systems: A MINLP-based preventive maintenance planning.IEEE Access,2020;8: 22324-22334, https://doi.org/10.1109/ACCESS.2020.2969997
  • 14. Deptuła AM, Stosiak M, Deptuła A, Lubecki M, Karpenko M, Skačkauskas P, Urbanowicz K, Danilevičius A. Risk Assessment of Innovation Prototype for the Example Hydraulic Cylinder. Sustainability. 2023; 15(1):440. https://doi.org/10.3390/su15010440
  • 15. Dhawalikar MN, Mariappan V, Srividhya PK, Kurtikar V. Multi-state failure phenomenon and analysis using semi-Markov model.International Journal of Quality & Reliability Management 2018, https://doi.org/10.1108/IJQRM-01-2016-0001
  • 16. Du X, Yang Z, Chen C, Li X, Pecht MG. Reliability analysis of repairable systems based on a two-segment bathtub-shaped failure intensity function.IEEE Access,2018;6:52374-52384, https://doi.org/10.1109/ACCESS.2018.2869704
  • 17. Elusakin T, Shafiee, M. Reliability analysis of subsea blowout preventers with condition-based maintenance using stochastic Petri nets.Journal of Loss Prevention in the Process Industries,2020;63: 104026, https://doi.org/10.1016/j.jlp.2019.104026
  • 18. Fallahnezhad MS, Ranjbar A, Saredorahi FZ. A Markov Model for production and maintenance decision.Macro Management & Public Policies 2020;2(1), https://doi.org/10.30564/mmpp.v2i1.655
  • 19. Fan Q, Fan H. Reliability analysis and failure prediction of construction equipment with time series models. Journal of Advanced Management Science 2015:3(3), https://doi.org/10.12720/joams.3.3.203-210
  • 20. Figueredo GP, Owa K, John R. Multi-objective optimization for time-based preventive maintenance within the transport network: a review.Academic and Library Computing 2020.
  • 21. Gertsbakh IB, Shpungin Y. Models of network reliability: analysis, combinatorics, and Monte Carlo. CRC press 2016 https://doi.org/10.1201/b12536
  • 22. Ghiasi M, Ghadimi N, Ahmadinia E. An analytical methodology for reliability assessment and failure analysis in distributed power system.SN Applied Sciences2019; 1(1): 1-9, https://doi.org/10.1007/s42452-018-0049-0
  • 23. Gracheva EI, Fedorov OV. Forecasting reliability electrotechnical complexes of in-plant electric power supply taking into account low-voltage electrical apparatuses. In2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)2019:1-5, https://doi.org/10.1109/ICIEAM.2019.8743057
  • 24. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning, Springer-Verlag New York Inc. 2009, https://doi.org/10.1007/978-0-387-84858-7
  • 25. Huang D, Lanyan K, Chu X, Zhao L, Mi B. Fault diagnosis for the motor drive system of urban transit based on improved Hidden Markov Model. Microelectronics Reliability 2018;82:179–89, https://doi.org/10.1016/j.microrel.2018.01.017
  • 26. Huang J, Chang Q, Arinez J. Deep reinforcement learning based preventive maintenance policy for serial production lines.Expert Systems with Applications 2020;160:113701, https://doi.org/10.1016/j.eswa.2020.113701
  • 27. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning, Springer-Verlag GmbH 2013, https://doi.org/10.1007/978-1-4614-7138-7
  • 28. Jasiulewicz-Kaczmarek M, Antosz K. The concept of sustainable maintenance criteria assessment. InIFIP International Conference on Advances in Production Management Systems2021;427-436 Springer, Cham, https://doi.org/10.1007/978-3-030-85910-7_45
  • 29. Kobayashi H, Mark BL, Turin W. Probability, random processes, and statistical analysis, Cambridge University Press 2011, https://doi.org/10.1017/CBO9780511977770
  • 30. Kouadri A, Hajji M, Harkat MF, Abodayeh K, Mansouri M, Nounou H, Nounou M. Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems.Renewable Energy2020;150:598-606, https://doi.org/10.1016/j.renene.2020.01.010
  • 31. Kuboń M, Kaczmar I, Findura P. Reliability of technical systems and the methodology for calculating MTBF using Flexsim computer simulation. InE3S Web of Conferences2019;132:01012.
  • 32. Kumaresan K, Ganeshkumar P. Software reliability prediction model with realistic assumption using time series (S) ARIMA model. Journal of Ambient Intelligence and Humanized Computing 2020;11(11):5561-5568, https://doi.org/10.1007/s12652-020-01912-4
  • 33. Kusyi Y, Stupnytskyy V, Onysko O, Dragašius E, Baskutis S, Chatys R. Optimization synthesis of technological parameters during manufacturing of the parts. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022;24(4):655-667. https://doi.org/10.17531/ein.2022.4.6
  • 34. Lazarova-Molnar S, Niloofar P, Barta, GK. Automating reliability analysis: Data-driven learning and analysis of multi-state fault trees. In30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. Research Publishing Services2020;1805-1812, https://doi.org/10.1016/j.renene.2018.08.048
  • 35. Lewandowski J, Młynarski S, Pilch R, Smolnik M, Szybka J, Wiązania G. An evaluation method of preventive renewal strategies of railway vehicles selected parts. Eksploatacja i Niezawodnosc –Maintenance and Reliability 2021; 23 (4): 678–684, http://doi.org/10.17531/ ein.2021.4.10
  • 36. Li J, Zhang X, Zhou X, Lu L. Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model.Renewable energy 2019;132:1076-1087, https://doi.org/10.1016/j.renene.2018.08.048
  • 37. Li L, Wang Y, Lin KY. Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization.Journal of Intelligent Manufacturing,2021; 32(2):545-558, https://doi.org/10.1007/s10845-020-01588-9
  • 38. Li M, Wang Z. Deep learning for high-dimensional reliability analysis.Mechanical Systems and Signal Processing2020;139:106399, https://doi.org/10.1016/j.ymssp.2019.106399
  • 39. Li W, Cheng M. Reliability analysis and evaluation for flux-switching permanent magnet machine.IEEE Transactions on Industrial Electronics,2018;66(3):1760-1769, https://doi.org/10.1109/TIE.2018.2838105
  • 40. Li Y, Dong Y, Zhang H, Zhao H, Shi H, Zhao X. Spectrum Usage Prediction Based on High-order Markov Model for Cognitive Radio Networks. 10th IEEE International Conference on Computer and Information Technology, Bradford 2010; 2784-2788. doi: 10.1109/CIT.2010.464
  • 41. Li Z, Liu L, Kong D. Virtual machine failure prediction method based on AdaBoost-Hidden Markov model. In2019 International Conference on Intelligent Transportation, Big Data & Smart City 2019; 700-703, https://doi.org/10.1109/ICITBS.2019.00173
  • 42. Lin S, Li N, Feng D, Guo X, Pan W, Wang J, Yang C. A preventive opportunistic maintenance method for railway traction power supply system based on equipment reliability.Railway Engineering Science,2020;28(2):199-211, https://doi.org/10.1007/s40534-020-00211-0
  • 43. Lisnianski A, Frankel I. Non-homogeneous Markov reward model for an ageing multi-state system under minimal repair, International Journal of Performability Engineering 2009;5(4):303-312;
  • 44. Liu H, Xiao N. Global non-probabilistic reliability sensitivity analysis based on surrogate model. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022;24(4):612-616. https://doi.org/10.17531/ein.2022.4.2
  • 45. Mustafa MN. Classification of maintenance techniques and diagnosing failures methods. InJournal of Physics: Conference Series2021;2060(1):012014). IOP Publishing, https://doi.org/10.1088/1742-6596/2060/1/012014
  • 46. Naess A, Leira BJ, Batsevych O. System reliability analysis by enhanced Monte Carlo simulation.Structural safety 2009;31(5):349-355, https://doi.org/10.1016/j.strusafe.2009.02.004
  • 47. Nejad AR, Gao Z, Moan T. On long-term fatigue damage and reliability analysis of gears under wind loads in offshore wind turbine drivetrains.International Journal of Fatigue,2014;61:116-128, https://doi.org/10.1016/j.ijfatigue.2013.11.023
  • 48. Ni P, Li J, Hao H, Yan W, Du X, Zhou H. Reliability analysis and design optimization of nonlinear structures.Reliability Engineering & System Safety,2020;198:106860, https://doi.org/10.1016/j.ress.2020.106860
  • 49. Oszczypała M, Ziółkowski J, Małachowski J. Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach. Energies. 2023; 16(5):2210. https://doi.org/10.3390/en16052210
  • 50. Pandey P, Mukhopadhyay AK, Chattopadhyaya S. Reliability analysis and failure rate evaluation for critical subsystems of the dragline.Journal of the brazilian society of mechanical sciences and engineering,2018;40(2):1-11, https://doi.org/10.1007/s40430-018-1016-9
  • 51. Parzen E. On estimation of a probability density function and mode, The Annals of Mathematical Statistics. 1962;33:1065–1076. https://doi.org/10.1214/aoms/1177704472.
  • 52. Pawlik P., Kania K., Przysucha B., Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25 (3): https://doi.org/10.17531/ein/168109
  • 53. Perman M, Senegacnik A, Tuma M. Semi-Markov models with an application to power-plant reliability analysis, inIEEE Transactions on Reliability, 1997;46(4):526-532, doi: 10.1109/24.693787
  • 54. Qiu Q, Cui L, Yang L. Maintenance policies for energy systems subject to complex failure processes and power purchasing agreement.Computers & Industrial Engineering,2018;119:193-203, https://doi.org/10.1016/j.cie.2018.03.035
  • 55. Ran Y, Zhou X, Lin P, Wen Y, Deng R. Survey of Predictive Maintenance: Systems, Purposes and Approaches Computer Science, 2019.
  • 56. Rosenblatt M. Remarks on some nonparametric estimates of a density function, The Annals of Mathematical Statistics. 1956;26:832–837, https://doi.org/10.1214/aoms/1177728190
  • 57. Ross S.M. Introduction to probability models, Academic press 1997.
  • 58. Rusin A, Tomala M. Steam turbine maintenance planning based on forecasting of life consumption processes and risk analysis. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022;24(3):1. https://doi.org/10.17531/ein.2022.3.1
  • 59. Rydén T, Teräsvirta T. Åsbrink S. Stylized facts of daily return series and the hidden Markov model. Journal of Applied Econometrics, 1998;13:217-244. doi:10.1002/(SICI)1099-1255(199805/06)13:3<217::AID-JAE476>3.0.CO;2-V
  • 60. Sánchez-Herguedas A, Mena-Nieto Á, Rodrigo-Muñoz F. A method for obtaining the preventive maintenance interval in the absence of failure time data. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022;24(3):17. https://doi.org/10.17531/ein.2022.3.17
  • 61. Sanusi W, Jemain AA, Zin WZ, Zahari M. The Drought Characteristics Using the First-Order Homogeneous Markov Chain of Monthly Rainfall Data in Peninsular Malaysia, Water Resources Management 2015;29(5): 1523–1539, https://doi.org/10.1007/s11269-014-0892-8
  • 62. Serradilla O, Zugasti E, Rodriguez J, Zurutuza U. Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects.Applied Intelligence, 2022;1-31, https://doi.org/10.1007/s10489-021-03004-y
  • 63. Shang L, Zou A, Qiu Q, Du Y. A random maintenance last model with preventive maintenance for the product under a random warranty. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022;24(3):15. https://doi.org/10.17531/ein.2022.3.15
  • 64. Shi S. et al. Research on Markov Property Analysis of Driving Cycle, 2013 IEEE Vehicle Power and Propulsion Conference (VPPC), Beijing, 2013;1-5. doi: 10.1109/VPPC.2013.6671737
  • 65. Shumway RH, Stoffer DS. Time series analysis and its applications: With R examples, Springer 2017, https://doi.org/10.1007/978-3-319-52452-8
  • 66. Silverman BW. Density estimation for statistics and data analysis. Chapman & Hall, New York 1986
  • 67. Sobaszek Ł, Gola A, Kozłowski E. Predictive scheduling with Markov chains and ARIMA models. Applied Sciences,2020;10(17):6121, https://doi.org/10.3390/app10176121
  • 68. van Casteren JFL,Bollen M, Schmieg M. Reliability assessment in electrical power systems: the Weibull-Markov stochastic model," 1999 IEEE Industrial and Commercial Power Systems Technical Conference, Sparks, NV, USA, 1999;5, doi: 10.1109/ICPS.1999.787215.
  • 69. Wang X, Zhou H, Parlikad AK, Xie M. Imperfect preventive maintenance policies with unpunctual execution.IEEE Transactions on Reliability 2020;69(4):1480-1492, https://doi.org/10.1109/TR.2020.2983415
  • 70. Wang Y, Infield D. Markov Chain Monte Carlo simulation of electric vehicle use for network integration studies, InternationalJournal of Electrical Power & Energy Systems 2018;99:85-94, doi.org/10.1016/j.ijepes.2018.01.008
  • 71. Wang Z, Huang X, Liang YJ. Oil-gas reservoir lithofacies stochastic modelling based on one-to three-dimensional Markov chains. Journal of Central South University 2018; 25(6): 1399–1408, https://doi.org/10.1007/s11771-018-3835-3
  • 72. Yang H, Li W, Wang B. Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning.Reliability Engineering & System Safety,2021;214:107713, https://doi.org/10.1016/j.ress.2021.107713
  • 73. Yang L, Zhao Y, Peng R, Ma X. Hybrid preventive maintenance of competing failures under random environment.Reliability Engineering & System Safety,2018;174:130-140, https://doi.org/10.1016/j.ress.2018.02.017
  • 74. Zhang J, Kong X, Cheng L, Qi H, Yu M, Intelligent faultdiagnosis of rolling bearings based on continuous wavelet transformmultiscale feature fusion and improved channel attention mechanism. Eksploatacja i Niezawodnosc –Maintenance and Reliability 2023: 25(1) http://doi.org/10.17531/ein.2023.1.16
  • 75. Zhang T, LiuT, Liu D, Sun F. Reliability Evaluation of Modular Multilevel Converter System Based on Semi-markov Model. In2021 IEEE 4th International Conference on Electronics Technology (ICET) 2021; 513-517, https://doi.org/10.1109/ICET51757.2021.9450902
  • 76. Zhang Y, Zhang Q, Yu R. Markov property of Markov chains and its test,2010 International Conference on Machine Learning and Cybernetics, Qingdao, 2010:1864-1867, doi: 10.1109/ICMLC.2010.5580952
  • 77. Zonta T, da Costa CA, da Rosa Righi R, de Lima MJ, da Trindade ES, Li GP. Predictive maintenance in the Industry 4.0 A systematic literature review. Computers & Industrial Engineering,2020;150:106889, https://doi.org/10.1016/j.cie.2020.106889
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1dbc594e-aeef-43c8-9269-93a7c6cb9444
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.