A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system |
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| Institution: | 1. Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China, China;2. The Israel Electric Corporation, Haifa, Israel;3. University of the Free State, Bloemfontein, South Africa;4. ITMO University, St. Petersburg, Russia;1. Collaborative Autonomic Computing laboratory, University of Electronic Science and Technology of China, Chengdu 611731, China;2. The Israel Electric Corporation, P. O. Box 10, Haifa 31000, Israel;3. University of the Free State, Bloemfontein, South Africa;4. ITMO University, St. Petersburg, Russia;1. ICD-LM2S, Université de Technologie de Troyes, Troyes, France;2. Norwegian University of Science and Technology, Trondheim, Norway;1. Department of Information Service, Xi''an Communications Institute, Xi''an 710106, China;2. Department of Basic Courses, Xi''an Communications Institute, Xi''an 710106, China |
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| Abstract: | We consider a repairable system subject to a continuous-time Markovian deterioration while running, that leads to failure. The deterioration degree is measured with a finite discrete scale; repairs follow general distributions; failures are instantaneously detected. This system is submitted to a preventive maintenance policy, with a sequential checking procedure: the up-states are divided into two parts, the “good” up-states and the “degraded” up-states. Instantaneous (and perfect) inspections are then performed on the running system: when it is found in a degraded up-state, it is stopped to be maintained (for a random duration that depends on the degradation degree of the system); when it is found in a good up-state, it is left as it is. The next inspection epoch is then chosen randomly and depends on the degradation degree of the system by time of inspection. We compute the long-run availability of the maintained system and give sufficient conditions for the preventive maintenance policy to improve the long-run availability. We study the optimization of the long-run availability with respect to the distributions of the inter-inspection intervals: we show that under specific assumptions (often checked), optimal distributions are non-random. Numerical examples are studied. |
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