مقاله انگلیسی رایگان در مورد استراتژی تعمیر و نگهداری پویا – الزویر ۲۰۲۰
مشخصات مقاله | |
ترجمه عنوان مقاله | استراتژی تعمیر و نگهداری پویا با اطلاعات گروهی تکراری و به روز |
عنوان انگلیسی مقاله | Dynamic maintenance strategy with iteratively updated group information |
انتشار | مقاله سال ۲۰۲۰ |
تعداد صفحات مقاله انگلیسی | ۴۰ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۵٫۱۹۱ در سال ۲۰۱۹ |
شاخص H_index | ۱۱۹ در سال ۲۰۲۰ |
شاخص SJR | ۱٫۹۴۴ در سال ۲۰۱۹ |
شناسه ISSN | ۰۹۵۱-۸۳۲۰ |
شاخص Quartile (چارک) | Q1 در سال ۲۰۱۹ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی صنایع |
گرایش های مرتبط | برنامه ریزی و تحلیل سیستم ها، بهینه سازی سیستم ها |
نوع ارائه مقاله |
ژورنال |
مجله | مهندسی قابلیت اطمینان و ایمنی سیستم – Reliability Engineering & System Safety |
دانشگاه | School of Reliability and Systems Engineering, Beihang University, Beijing, China |
کلمات کلیدی | تعمیر و نگهداری پویا، تعمیر و نگهداری فرصت طلبانه، گروه بندی تعمیر و نگهداری، سیستم چند جزئی |
کلمات کلیدی انگلیسی | dynamic maintenance, opportunistic maintenance, maintenance grouping, multi-component system |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.ress.2020.106820 |
کد محصول | E14465 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract Nomenclature ۱٫ Introduction ۲٫ Literature review ۳٫ Problem statement ۴٫ Dynamic maintenance grouping approach ۵٫ Extension to condition-based maintenance ۶٫ Experimental validation ۷٫ Conclusions Author statement Declaration of Competing Interest Acknowledgment Appendix A Reference |
بخشی از متن مقاله: |
Abstract
Maintenance grouping methods such as the rolling horizon approach are effective in reducing maintenance costs of multi-component systems. Despite the theoretical advancements of this approach, it still faces three challenges. First, the extensively adopted minimal repair assumption upon failures limits its application. Second, opportunistic maintenance upon corrective maintenance is overlooked, unable to fully take advantage of economic dependence. Third, maintenance plans are not based on actual maintenance history and health information, which may increase failure risks. To address these challenges, this paper formulates a novel dynamic planning framework that captures economic dependence in both preventive and opportunistic replacement. Unlike conventional approaches that restrict all maintenance activities into a finite planning horizon, our proposal focuses on activity-to-activity scheduling without specifying the horizon. As such, the subsequent maintenance schedule is dynamically updated once a system maintenance is executed. A flexible dynamic programming algorithm is developed to optimize the maintenance grouping, and the strategy framework is further extended to condition-based maintenance scenarios. The effectiveness and generality of the proposed maintenance strategy are demonstrated by numerical experiments. Introduction Diverse industrial systems, such as smart grids, wind farms and high-speed trains are subject to multiple interdependencies existing among components or sub-systems [1]. Typically, there are three categories of dependencies, i.e. economic dependence [2], stochastic dependence [3], and structural dependence [4, 5]. Among them, the economic dependence attracts the most notable attention due to its significant impact on system operations & maintenance costs [6]. Such dependence allows to share set-up and downtime costs when multiple components are maintained simultaneously, so that maintenance resources can be significantly harnessed [7]. Group maintenance [2, 8-10] and opportunistic maintenance (OM) [11-14] are two representative maintenance policies taking advantage of the economic dependence. The former specifies a pre-determined schedule for inspections or preventive maintenance (PM), while the latter provides PM opportunities for other components when a component undergoes preventive or corrective maintenance (CM). Notably, OM of multi-component systems is generally scheduled based on operational age and/or the reliability level of components. This triggers tremendous operational states and brings difficulties for the analytical modelling [11, 15, 16]. Consequently, many OM policies are optimized via simulations [11, 17, 18], which is trivial and time-consuming. In this regard, recently a few researches employed (deep) reinforcement learning (RL) methods to address this problem [19-22]. |