برنامه ریزی پیشگیرانه تعمیر و نگهداری راکت موتور با قابلیت استفاده مجدد با استفاده از الگوریتم ژنتیک
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|8177||2013||9 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Reliability Engineering & System Safety, Volume 114, June 2013, Pages 52–60
This paper deals with the preventive maintenance (PM) scheduling problem of reusable rocket engine (RRE), which is different from the ordinary repairable systems, by genetic algorithm. Three types of PM activities for RRE are considered and modeled by introducing the concept of effective age. The impacts of PM on all subsystems' aging processes are evaluated based on improvement factor model. Then the reliability of engine is formulated by considering the accumulated time effect. After that, optimization model subjected to reliability constraint is developed for RRE PM scheduling at fixed interval. The optimal PM combination is obtained by minimizing the total cost in the whole life cycle for a supposed engine. Numerical investigations indicate that the subsystem's intrinsic reliability characteristic and the improvement factor of maintain operations are the most important parameters in RRE's PM scheduling management.
Maintenance is essential to keep a system in normal condition during the life cycle. For most systems, it could be classified into two categories, corrective maintenance (CM) and preventive maintenance (PM). CM is performed after failure to return the system to service as soon as possible. As for PM, it usually includes a well-defined set of tasks such as periodical inspection, cleaning, lubrication, adjusting, repairing of subcomponent and replacement, which help to enhance the state of the system by changing the age characteristic  and . CM and PM are often carried out together to improve the reliability and availability of the system. For a reusable rocket engine (RRE) like space shuttle main engine (SSME), which is designed to perform several flight tasks  and , the situation would be much different. Since RRE is retrievable after launch, by using RRE, the customers need not pay for a traditional expendable rocket engine each time, which is wasteful. The cost is reduced by sharing the total expense in an engine's service life. However, this means the engine has to survive all the prescribed flights, rendering high reliability a crucial requirement for RRE. From the view point of maintainability engineering, the potential disastrous consequences of rocket engine failure would eliminate repair or corrective maintenance as a primary consideration. Moreover, CM could not even be incorporated in PM scheduling in this case for the succeeding explosion or destructive effect after failure. Concern would be with the trade-off between RRE's reliability and how much PM could afford. PM scheduling often involves balancing between reliability and economic performance, and is especially complicated for complex system with many components. This topic has been extensively discussed in the past decades , , , , , , , , ,  and . Researchers tackle this problem from two perspectives: one is to construct PM models using single-component deteriorating system, the other is to solve the multi-components PM scheduling problem on the basis of single-component PM techniques. Nakagawa  and Kijima  present notable research for PM models by introducing effective age notation, which is also called virtual age model. Component which benefits from PM can have an effective age less than its calendar age. Tsai and Wang  propose a method to evaluate the time-dependent effect of three typical PM actions on component reliability. Moghaddam and Usher  use the branch and bound method to plan PM and replacement activities for a repairable and maintainable complex system. A growing number of heuristic algorithms are employed for complex system PM scheduling to consider the trade-off between reliability and cost , ,  and . In this paper, given the characteristic of RRE mentioned above, an optimization model is developed for PM scheduling at fixed interval considering no CM. Basic genetic algorithm is modified to determine the optimal PM practice by minimizing the total cost under the system reliability constraint. The remainder of the paper is organized as follows. In Section 2, the system configuration of RRE is introduced. Section 3 gives a detailed description of optimization model for RRE PM scheduling. Three kinds of PM activities are taken into account simultaneously in this part. After that, subsystem's aging processes and time-dependent reliabilities are modeled. Section 4 is devoted to GA modification for solving the model. The computational results are then presented in Section 5. Some conclusion remarks are provided in Section 6.
نتیجه گیری انگلیسی
Reliability has long been of great concern in space industry, which is especially crucial for reusable rocket engine. While the preventive maintenance scheduling for RRE is different from the other repairable systems, we put forward a new model to handle the problem. The model could also benefit the systems with similar features including but not limited to military plane, unmanned air vehicle (UAV), the crucial components in nuclear plant, etc. In this paper, we employed the effective age approach to mode three kinds of PM activities for RRE. The multi-components system's reliability was evaluated considering the accumulated effect. An optimization model subjected to reliability constraint was then developed for RRE PM scheduling at fixed interval with no CM. The objective was to minimize the total cost during the life cycle of the RRE, comprising not only the PM operation cost, but also the fix cost and potential failure cost. Genetic algorithm was then modified to solve the model. An optimal PM schedule was obtained as well as the time-dependent system reliability. MR is the main maintenance operation to keep RRE in normal condition.The overhaul period could be determined from the schedule. It could be concluded that the subsystem's intrinsic reliability characteristic and the improvement factor of maintain operations are the most important parameters in PM scheduling management for RRE. The subsystem with a large MTBF tends to receive less PM in the life cycle, and vice versa.