RESEARCH PAPER
Balancing reliability and maintenance cost rate of multi-state components with fault interval omission
1
College of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing 211106, Jiangsu, PR China
Publication date: 2019-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(1):37-45
KEYWORDS
multi-state systems (MSSs)fault effect omissionstochastic processrepairable modelmaintenance cost rate
ABSTRACT
For the repairable multi-state component, reliability indexes are analyzed based on a homogenous Continuous Time Markov
Chain (CTMC). If the component can work well when its repair time is sufficiently short, a threshold value for maintenance is
introduced. When the fault interval is less than threshold time, the fault effect is considered neglected. In this paper, comparisons of
availability show differences of the new model and the original model with or without fault interval omission. In addition, balancing the maintenance cost and lifetime of multi-state components is an important issue when threshold values are considered. Both
constants and non-negative random variables are modeled respectively. Finally, numerical examples are presented to illustrate
the results obtained in this paper
REFERENCES (30)
1.
Bao X, Cui L. An Analysis of Availability for Series Markov Repairable System With Neglected or Delayed Failures. IEEE Transactions on Reliability 2010; 59(4):734-743, https://doi.org/10.1109/TR.201....
2.
Caldarod L. Coherent systems with multistate components. Nuclear Engineering & Design 1980; 58(1):127-139, https://doi.org/10.1016/0029- 5493(80)90102-8.
3.
Calderhead B, Epstein M, Sivilotti L, et al. Bayesian Approaches for Mechanistic Ion Channel Modeling. Methods Mol Biol. 2013; 1021: 247-272, https://doi.org/10.1007/978-1-....
4.
Calimet N, Simoes M, Changeux J P, et al. A gating mechanism of pentameric ligand-gated ion channels. Proceedings of the National Academy of Sciences of the United States of America 2013; 110 (42): 3987-3996, https://doi.org/10.1073/pnas.1....
5.
Cui L, Zhang Q, Kong D. Some New Concepts and Their Computational Formulae in Aggregated Stochastic Processes with Classifications Based on Sojourn Times. Methodology & Computing in Applied Probability 2015; 18(4): 1-21.
6.
Dong W, Liu S, Fang Z et al. A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2018, https://doi. org/10.1177/1748006X18788414.
7.
Grabski F. Semi-Markov Processes: Applications in System Reliability and Maintenance. Graphical models and image processing. Academic Press 2014: 161-176.
8.
He Q M. Fundamentals of Matrix-Analytic Methods. Springer New York, 2014, https://doi.org/10.1007/978-1-....
9.
He Q, Zha Y, Zhang R et al. Reliability analysis for multi-state system based on triangular fuzzy variety subset bayesian networks. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; 19(2): 152-165, https://doi.org/10.17531/ein.2....
10.
Hirose H, Sakumura T. The Extended Cumulative Exposure Model (ECEM) and Its Application to Oil Insulation Tests. IEEE Transactions on Reliability 2012; 61(3): 625-633, https://doi.org/10.1109/TR.201....
11.
Cao J H, Cheng K. Introduction to Reliability Mathematics. Beijing: Science Press, 1986, (In Chinese).
12.
Jia X, Shen J, Xing R. Reliability Analysis for Repairable Multistate Two-Unit Series Systems When Repair Time Can Be Neglected. IEEE Transactions on Reliability 2016; 65(1): 208-216, https://doi.org/10.1109/TR.201....
13.
Levitin G. The Universal Generating Function in Reliability Analysis and Optimization. Springer, 2005.
14.
Levitin G, Amari S V. Multistate systems with multi-fault coverage. Reliability Engineering & System Safety 2017; 93 (11): 1730-1739, https://doi.org/10.1016/j.ress....
15.
Li M, Ma X, Zhang X et al. Reliability analysis of non-repairable cold-standby system based on the Wiener process. International Conference on System Reliability and Safety 2017: 151-155.
16.
Lisnianski A, Elmakias D, Laredo D et al. A multi-state Markov model for a short-term reliability analysis of a power generating unit. Reliability Engineering & System Safety 2017; 98(1): 1-6, https://doi.org/10.1016/j.ress....
17.
Lisnianski A, Frenkel I, Karagrigorious A. Recent Advances in Multi-state Systems Reliability. Springer 2018, https://doi.org/10.1007/978- 3-319-63423-4.
18.
Liu B, Cui L, Wen Y. Interval reliability for aggregated Markov repairable system with repair time omission. Annals of Operations Research 2014; 212(1): 169-183, https://doi.org/10.1007/s10479....
19.
Pan D, Liu J B, Huang F, et al. A Wiener Process Model With Truncated Normal Distribution for Reliability Analysis. Applied Mathematical Modelling 2017; 50: 333-346.
20.
Pham T, Pham H. A generalized software reliability model with stochastic fault-detection rate. Annals of Operations Research 2017; (4): 1-11, https://doi.org/10.1007/s10479....
21.
Pirious P Y, Faure J M, Lesage J J. Generalized Boolean logic Driven Markov Processes: A powerful modeling framework for Model-Based Safety Analysis of dynamic repairable and reconfigurable systems. Reliability Engineering & System Safety 2017; 163: 57-68, https://doi. org/10.1016/j.ress.2017.02.001.
22.
Qin J, Niu Y, Li Z. A combined method for reliability analysis of multi-state system of minor-repairable components. Eksploatacja i Niezawodnosc- Maintenance and Reliability 2016; 18 (1): 80-88, https://doi.org/10.17531/ein.2....
23.
Wang W, Maio F D, Zio E. Three-Loop Monte Carlo Simulation Approach to Multi-State Physics Modeling for System Reliability Assessment. Reliability Engineering & System Safety 2017; 167: 276-289, https://doi.org/10.1016/j.ress....
24.
Bao X Z, Cui L. A Study on Reliability for A Two-Item Cold Standby Markov Repairable System with Neglected Failures. Communications in Statistics 2012; 41(21): 3988-3999, https://doi.org/10.1080/036109....
25.
Yu H, Yang J, Mo H. Reliability analysis of repairable multi-state system with common bus performance sharing. Reliability Engineering & System Safety 2014; 132: 90-96, https://doi.org/10.1016/j.ress....
26.
Zaitseva E, Levashenko V. Multiple-Valued Logic mathematical approaches for multi-state system reliability analysis. Journal of Applied Logic 2013; 11(3): 350-362, https://doi.org/10.1016/j.jal.....
27.
Zhang C, Lu X, Tan Y, et al. Reliability demonstration methodology for products with Gamma Process by optimal accelerated degradation testing. Reliability Engineering & System Safety, 2015; 142:369-377, https://doi.org/10.1016/j.ress....
28.
Zhang Q, Cui L, Yi H. A study on a single-unit repairable system with working and repair time omission under an alternative renewal process. Journal of Risk & Reliability 2017; 231(1): 1748006X1769351.
29.
Zhao X, Qian C, Nakagawa T. Comparisons of Replacement Policies with Periodic Times and Repair Numbers. Reliability Engineering & System Safety 2017; 168: 161-170, https://doi.org/10.1016/j.ress....
30.
Zheng Z, Cui L, Hawkes A G. A study on a single-unit Markov repairable system with repair time omission. IEEE Transactions on Reliability 2006; 55(2): 182-188, https://doi.org/10.1109/TR.200....
CITATIONS (11):
1.
Reliability Variation and Optimal Age Replacement Schedule of Compensated Discrete Multi-state Systems
Wenjie Dong, Sifeng Liu, Suk Bae
2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA)
Wenjie Dong, Sifeng Liu, Suk Bae
2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA)
2.
Car reliability analysis based on periodic technical tests
Paweł Dziedziak, Tomasz Szczepański, Andrzej Niewczas, Marcin Ślęzak
Open Engineering
Paweł Dziedziak, Tomasz Szczepański, Andrzej Niewczas, Marcin Ślęzak
Open Engineering
3.
Reliability variation of multi-state components with inertial effect of deteriorating output performances
Wenjie Dong, Sifeng Liu, Liangyan Tao, Yingsai Cao, Zhigeng Fang
Reliability Engineering & System Safety
Wenjie Dong, Sifeng Liu, Liangyan Tao, Yingsai Cao, Zhigeng Fang
Reliability Engineering & System Safety
4.
Reliability modeling and optimal random preventive maintenance policy for parallel systems with damage self-healing
Wenjie Dong, Sifeng Liu, Yingsai Cao, Saad Javed, Yangyang Du
Computers & Industrial Engineering
Wenjie Dong, Sifeng Liu, Yingsai Cao, Saad Javed, Yangyang Du
Computers & Industrial Engineering
5.
Optimal periodic maintenance policies for a parallel redundant system with component dependencies
Wenjie Dong, Sifeng Liu, Yangyang Du
Computers & Industrial Engineering
Wenjie Dong, Sifeng Liu, Yangyang Du
Computers & Industrial Engineering
6.
A multi-stage imperfect maintenance strategy for multi-state systems with variable user demands
Wenjie Dong, Sifeng Liu, Suk Bae, Yu Liu
Computers & Industrial Engineering
Wenjie Dong, Sifeng Liu, Suk Bae, Yu Liu
Computers & Industrial Engineering
7.
A simulation approach on reliability assessment of complex system subject to stochastic degradation and random shock
Mohammad Pourhassan, Sadigh Raissi, Ashkan Hafezalkotob
Eksploatacja i Niezawodność – Maintenance and Reliability
Mohammad Pourhassan, Sadigh Raissi, Ashkan Hafezalkotob
Eksploatacja i Niezawodność – Maintenance and Reliability
8.
Forecasting the readiness of special vehicles using the semi-Markov model
Anna Borucka, Andrzej Niewczas, Kamila Hasilova
Eksploatacja i Niezawodność – Maintenance and Reliability
Anna Borucka, Andrzej Niewczas, Kamila Hasilova
Eksploatacja i Niezawodność – Maintenance and Reliability
9.
Durability and exploitation performance of cutting tools made out of chromium oxide nanocomposite materials
Edvin Gevorkyan, Mykola Prokopiv, Mirosław Rucki, Dmitrij Morozow
Eksploatacja i Niezawodność – Maintenance and Reliability
Edvin Gevorkyan, Mykola Prokopiv, Mirosław Rucki, Dmitrij Morozow
Eksploatacja i Niezawodność – Maintenance and Reliability
10.
Maintenance Strategy for Different Values of System Components
Jie He
Journal of Physics: Conference Series
Jie He
Journal of Physics: Conference Series
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
