Authors:
Chun-Yuan Cheng
1
;
Renkuan Guo
2
and
Mei-Ling Liu
3
Affiliations:
1
Chaoyang University of Technology, Taiwan
;
2
University of Cape Town, South Africa
;
3
National Taipei University of Technology; Chaoyang University of Technology, Taiwan
Keyword(s):
Simulation method, Time-between-failure random variates, Preventive maintenance, Age reduction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Discrete Event Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Modeling, Simulation and Architectures
;
Planning and Scheduling
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
Based on the theoretical model, a numerical method is usually necessary for obtaining the optimal preventive maintenance (PM) policy for a deteriorating system since the theoretical model becomes complicated when the system’s hazard rate function is changed after each PM. It makes the application of the theoretical model not suitable for real cases. Moreover, the theoretical model assumes using infinite time span to obtain the long-term expected number of failures. Yet, in reality, the deteriorating systems always have a finite life time. Hence, an optimal solution might not be resulted as compared to the infinite time span. Therefore, we consider using the simulation method to obtain a range of the near-optimal PM policy. The critical step of the simulation method for obtaining a near-optimal PM policy is the generation of the random variates (RV). In this research, three methods are developed to generate the required RVs of the time-between-failures (TBF) for the finite-time
-span preventive maintenance model with age reduction effect. It is found that there are no significant differences among three proposed RV generating methods when comparing the dispersion of the generated RV’s. However, the rejection method is the simplest method for obtaining the near-optimal PM policies. Examples of the near-optimal PM policies are also presented in this paper.
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