Search for Author, Title, Keyword
Dynamic reliability analysis of a multi-state manufacturing system
More details
Hide details
Department of Statistics Ege University 35040, Bornova, Izmir, Turkey
Department of Business Administration Ege University, 35040, Bornova, Izmir, Turkey
Publication date: 2019-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(3):451-459
Dynamic reliability analysis of binary systems has been widely studied in case of homogeneous continuous time Markov process assumption in the literature. In this study, we evaluate dynamic performance of a multi-state rotor line of electric motors manufacturing system under non-homogeneous continuous time Markov process (NHCTMP) degradation by using lifetime distributions of seven workstations within the system. By means of this degradation process assumption we capture the effect of age on the state change of components in the analysis by means of time dependent transition rates between states of the workstations. Essentially this is typical of many systems and more practical to use in real life applications. The working principle is based on a three state structure. If all the machines within each workstation work, the workstation is defined as working with the full performance. Whenever at least one machine fails within each workstation, then the workstation is defined as working with partial performance. If all the machines in the workstation fail then the workstation is defined as failed. The lifetime properties of the workstations under NHCTMP assumption have been studied for this three-state structure of the workstations. The workstations are all working independently and nonidentically from each other and they are connected in series within the system.We especially performed an extensive application study based on the lifetime data regarding the seven workstations within a manufacturing system. Dynamic reliability results are also discussed for the system structure. Some performance characteristics are developed for both workstations and the system as well. Numerical results for the performance characteristics of those workstations and the system are provided and supported with some graphical illustrations.
Abou S. Performanceassessmentofmulti-state systems with critical failure modes: Application totheflotationmetallic arsenic circuit. Reliability Engineering and System Safety 2010; 95: 614-622,
Aven T, Jensen U. Stochastic models in reliability. New York:Springer Verlag, 1999,
Eryilmaz S. Mean residual and mean past lifetime of multi-state systems with identical components. IEEE Transactions on Reliability 2010;59(7): 644-649,
Eryilmaz S. Lifetime of multistate k-out-of-n systems. Quality and Reliability Engineering International 2014; 30(7): 1015-1022,
Eryilmaz S, Xie M. Dynamic modeling of general three-state k-out-of-n:G systems: Permanent-based computational results. Journal of Computational and Applied Mathematics 2014; 272: 97-106,
Guilani P P, Sharifi M, Niaki S T A, Zaretalab A. Reliability evaluation of non-reparable three-state systems using Markov model and its comparison with the UGF and the recursive methods. Reliability Engineering and System Safety 2014; 129: 29-35,
Hatoyama Y. Reliability analysis of 3-state systems. IEEE Transactions on Reliability 1979; R-28(5): 386-393,
Huang J, Zuo M J, Wu Y. Generalized multi-state k-out-of-n:G systems. IEEE Transactions on Reliability 2000; 49(1): 105-111,
Khatab A, Ait-Kadi D, Rezg N. Kronecker algebra for series–parallel multi-state systems reliability evaluation. International Journal of Production Research 2012; 50(13): 3572-3578,
Kou W, Zuo, M J. Optimal reliability modeling: principles and applications.New Jersey:John Wiley and Sons, 2003.
Levitin G, Lisnianski A. Importance and sensitivity analysis of multi-state systems using the universal generating function method. Reliability Engineering and System Safety 1999; 65: 271-282,
Lia Y Y, Chena Y, Yuana Z H, Tanga N, Kanga R. Reliability analysis of multi-state systems subject to failure mechanism dependence based on a combination method. Reliability Engineering and System Safety 2017; 166: 109-123,
Lisnianski A, Levitin G. Multi-state system reliability: assessment, optimization, applications. Singapore: World Scientific Pub. Co. Inc.,2003,
Lisnianski A. Extended block diagram method for a multi-state systemreliability assessment. Reliability Engineering and System Safety 2007; 92: 1601-1607,
Liu Y, Zuo MJ,Huang H Z. Dynamic reliability assessment for multi-state degraded systems. Chemical Engineering Transactions 2013;33:535-540.
Liu Y, Zuo M J, Li Y F, Huang H Z. Dynamic reliability assessment for multi-state systems utilizing system-level inspection data. IEEE Transactions on Reliability 2015; 64(4):1287-1299,
Liu Y W, Kapur K C. Reliability measures for dynamic multistate nonrepairable systems and their applications to system performance evaluation. IIE Transactions2006; 38(6): 511-520,
Liu Y W, Kapur K C. Customer's cumulative experience measures for reliability of non-repairable aging multi-state systems. Quality Technology Quantitative Management 2007; 4: 255-234,
Niknam S A, Sawhney R. A model for reliability analysis of multi-state manufacturing systems. International Journal of Quality and Reliability Management 2014; 31(8): 938-949,
Qin J, Niu Y, Li Z. A combined method for reliability analysis of multi-state systemof minor-repairable components. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2016; 18(1): 80-88,
Ross S M. Stochastic Prosesses, 2nd Edition.New York:John Wiley andSons, 1996.
Sheu S H, Zhang Z G. An optimal age replacement policy for multi-state systems. IEEE Transactions on Reliability 2013; 42(3): 722-735,
Shu M H, Hsu B M, Kapur K C. Dynamic performance measures for tools with multi-state wear processes and their applications for tool design and selection. International Journal of Production Research 2010; 48(16): 4725-4744,
Tian Z, Zuo M J, Yam R C M. Multi-state k-out-of-n systems and their performance evaluation. IIE Transactions 2009; 41: 32-44,
An algorithmic reliability evaluation approach for a multi-state k-out-of-n:G system with nonidentical and large number of components
Asli Kilic, Funda Iscioglu
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
A new approach in the mean residual lifetime evaluation of a multi-state system
Funda Iscioglu
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Evaluation of Machinery Readiness Using Semi-Markov Processes
Andrzej Świderski, Anna Borucka, Małgorzata Grzelak, Leszek Gil
Applied Sciences
Car reliability analysis based on periodic technical tests
Paweł Dziedziak, Tomasz Szczepański, Andrzej Niewczas, Marcin Ślęzak
Open Engineering
Innovations in Mechatronics Engineering
Edward Kozłowski, Anna Borucka, Yiliu Liu, Dariusz Mazurkiewicz
Application of Logistic Regression for Production Machinery Efficiency Evaluation
Anna Borucka, Małgorzata Grzelak
Applied Sciences
Performance reliability analysis and optimization of lithium-ion battery packs based on multiphysics simulation and response surface methodology
Quan Xia, Zili Wang, Yi Ren, Dezhen Yang, Bo Sun, Qiang Feng, Cheng Qian
Journal of Power Sources
Fault-tolerant design for increasing the reliability of an autonomous driving gear shifting system
Ralf Stetter, Richy Göser, Sebastian Gresser, Markus Till, Marcin Witczak
Eksploatacja i Niezawodność – Maintenance and Reliability
Forecasting the readiness of special vehicles using the semi-Markov model
Anna Borucka, Andrzej Niewczas, Kamila Hasilova
Eksploatacja i Niezawodność – Maintenance and Reliability
Digital Transformation in Industry
Evgeniy Kozin
Stochastic operation model for readiness assessment of small wind turbines based on Markov theory
Justyna Zalewska‐Lesiak, Jerzy Małachowski, Joanna Szkutnik‐Rogoż
IET Renewable Power Generation
Journals System - logo
Scroll to top