RESEARCH PAPER
Dynamic condition-based maintenance policy for degrading systems described by a random-coefficient autoregressive model: A comparative study
1
School of Automation Science and Electrical Engineering Beihang University Beijing, China, 100191
Publication date: 2018-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2018;20(4):590-601
KEYWORDS
degradation modelingautoregressive modelBayesian methodresidual life estimationsemi-Markov decision processcondition-based maintenance
ABSTRACT
In this paper, we optimize a dynamic condition-based maintenance policy for a slowly degrading system subject to soft failure and
condition monitoring at equidistant, discrete time epochs. A random-coefficient autoregressive model with time effect is developed
to describe the system degradation. The system age, previous state observations, and the item-to-item variability of the degradation are jointly combined in the proposed degradation model. Stochastic behavior for both the age-dependent and the statedependent term are considered, and a Bayesian approach for periodically updating the estimates of the stochastic coefficients is
developed to combine information from a degradation database with real-time condition-monitoring information. Based on this
degradation model, the dynamic maintenance policy is formulated and solved in a semi-Markov decision process framework. Incorporated with the same semi-Markov decision process framework is a novel approach for mean residual life estimation, which
enables simultaneous residual life estimation with the optimization procedure. The effectiveness of using the proposed randomcoefficient autoregressive model with time effect rather than the existing fixed-coefficient ones to describe system degradation is
demonstrated through a comparative study based on a real degradation dataset. The advantages of using a dynamic maintenance
policy are also revealed
REFERENCES (29)
1.
Benyamini Z, Yechiali U. Optimality of control limit maintenance policies under nonstationary deterioration. Probability in the Engineering and Informational Sciences 1999; 13(1): 55–70, https://doi.org/10.1017/S02699....
2.
Bergquist B, Soderholm P. Data analysis for condition-based railway in- frastructure maintenance. Quality & Reliability Engineering International 2015; 31(5): 773-781, https://doi.org/10.1002/qre.16....
3.
Besnard F, Bertling L. An approach for condition-based maintenance opti mization applied to wind turbine blades. IEEE Transactions on Sustainable Energy 2010; 1(2): 77–83, https://doi.org/10.1109/TSTE.2....
4.
Chen D, Trivedi K S. Optimization for condition-based maintenance with semi-Markov decision process. Reliability Engineering & System Safety 2005; 90(1): 25–29, https://doi.org/10.1016/j.ress....
5.
Chen N, Ye Z S, Xiang Y, Zhang L. Condition-based maintenance using the inverse Gaussian degradation model. European Journal of Operational Research 2015; 243(1): 190–199, https://doi.org/10.1016/j.ejor....
6.
Elwany A H, Gebraeel N Z, Maillart L M. Structured replacement policies for components with complex degradation processes and dedicated sensors. Operations research. 2011; 59(3): 684–695, https://doi.org/10.1287/opre.1....
7.
Gebraeel N Z, Pan J. Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE Transactions on Reliability 2008; 57(4): 539–550, https://doi.org/10.1109/TR.200....
8.
Giorgio M, Guida M, Pulcini G. A new class of Markovian processes for deteriorating units with state dependent increments and covariates. IEEE Transactions on Reliability 2015; 64(2): 562–578, https://doi.org/10.1109/TR.201....
9.
Giorgio M, Guida M, Pulcini G. An age-and state-dependent Markov model for degradation processes. IIE Transactions 2011; 43(9): 621–632, https://doi.org/10.1080/074081....
10.
Giorgio M, Pulcini G. A new state-dependent degradation process and related model misidentification problems. European Journal of Operational Research 2018; 267, https://doi.org/10.1016/j.ejor....
11.
Jiang R, Yu J, Makis V. Optimal Bayesian estimation and control scheme for gear shaft fault detection. Computers & Industrial Engineering 2012; 63(4): 754–762, https://doi.org/10.1016/j.cie.....
12.
Kaiser K A, Gebraeel N Z. Predictive maintenance management using sensor-based degradation models. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 2009; 39(4): 840–849, https://doi.org/10.1109/TSMCA.....
13.
Kim M J, Jiang R, Makis V, Lee CG. Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure. European Journal of Operational Research. 2011; 214(2): 331–339, https://doi.org/10.1016/j.ejor....
14.
Kim M J, Makis V. Optimal maintenance policy for a multi-state deteriorating system with two types of failures under general repair. Computers & Industrial Engineering 2009; 57(1): 298–303, https://doi.org/10.1016/j.cie.....
15.
Lin D, Wiseman M, Banjevic D, Jardine A K S. An approach to signal processing and condition-based maintenance for gearboxes subject to tooth failure. Mechanical Systems and Signal Processing 2004; 18(5): 993–1007, https://doi.org/10.1016/j.ymss....
16.
Meeker W Q, Escobar L A. Statistical methods for reliability data. John Wiley & Sons 2014.
17.
Moghaddass R, Zuo M J. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliability Engineering & System Safety 2014; 124: 92–104, https://doi.org/10.1016/j.ress....
18.
Papakonstantinou K G, Shinozuka M. Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory. Reliability Engineering & System Safety 2014; 130: 202–213, https://doi.org/10.1016/j.ress....
19.
Papakonstantinou K G, Shinozuka M. Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation. Reliability Engineering & System Safety 2014; 130: 214–224, https://doi.org/10.1016/j.ress....
21.
Si X S, Wang W, Hu C H, Zhou D H, Pecht MG. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Transactions on Reliability 2012; 61(1): 50–67, https://doi.org/10.1109/TR.201....
22.
Si X S, Wang W, Hu C H, Zhou D H. Estimating remaining useful life with three-source variability in degradation modeling. IEEE Transactions on Reliability 2014; 63(1): 167–190, https://doi.org/10.1109/TR.201....
23.
Tang D, Makis V, Jafari L, Yu J. Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring. Reliability Engineering & System Safety 2015; 134: 198–207, https://doi.org/10.1016/j.ress....
24.
Tang D, Yu J. Optimal replacement policy for a periodically inspected system subject to the competing soft and sudden failures. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2015; 17 (2): 228-235, http://dx.doi.org/10.17531/ein....
26.
Van Noortwijk J M. A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety 2009; 94(1): 2–21, https://doi.org/10.1016/j.ress....
27.
Zhang Z X, Si X S, Hu C H. An age- and state-dependent nonlinear prognostic model for degrading systems. IEEE Transactions on Reliability 2015; 64(4): 1214–1228, https://doi.org/10.1109/TR.201....
28.
Zhao X, Fouladirad Mi, Berenguer C, Bordes L. Condition-based inspection/ replacement policies for non-monotone deteriorating systems with environmental covariates. Reliability Engineering & System Safety 2010; 95(8): 921–934, https://doi.org/10.1016/j.ress....
29.
Zhou Y, Huang M. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMAmodel. Microelectronics Reliability 2016; 65: 265–273, https://doi.org/10.1016/j.micr....
CITATIONS (6):
1.
Integrating Modelling of Maintenance Policies within a Stochastic Hybrid Automaton Framework of Dynamic Reliability
Simone Arena, Irene Roda, Ferdinando Chiacchio
Applied Sciences
Simone Arena, Irene Roda, Ferdinando Chiacchio
Applied Sciences
2.
Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics
Jimenez Montero, Sébastien Schwartz, Rob Vingerhoeds, Bernard Grabot, Michel Salaün
Journal of Manufacturing Systems
Jimenez Montero, Sébastien Schwartz, Rob Vingerhoeds, Bernard Grabot, Michel Salaün
Journal of Manufacturing Systems
3.
METHOD OF TESTING THE READINESS OF MEANS OF TRANSPORT WITH THE USE OF SEMI-MARKOV PROCESSES
Anna Borucka
Transport
Anna Borucka
Transport
4.
Maintenance Optimization Model with Sequential Inspection Based on Real-Time Reliability Evaluation for Long-Term Storage Systems
Bai, Cheng, Guo
Processes
Bai, Cheng, Guo
Processes
5.
Rolling horizon optimal maintenance policy for a system subject to shocks and degradation under uncertain parameters
Firoozeh Haghighi, Bruno Castanier, Hasan Misaii
Computers & Industrial Engineering
Firoozeh Haghighi, Bruno Castanier, Hasan Misaii
Computers & Industrial Engineering
6.
Development and Validation of Concept of Innovative Method of Computer-Aided Monitoring and Diagnostics of Machine Components
Krzysztof Herbuś, Andrzej Dymarek, Piotr Ociepka, Tomasz Dzitkowski, Cezary Grabowik, Kamil Szewerda, Katarzyna Białas, Zbigniew Monica
Applied Sciences
Krzysztof Herbuś, Andrzej Dymarek, Piotr Ociepka, Tomasz Dzitkowski, Cezary Grabowik, Kamil Szewerda, Katarzyna Białas, Zbigniew Monica
Applied Sciences
| 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.
