سال انتشار:
2020
عنوان انگلیسی مقاله:
Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks
ترجمه فارسی عنوان مقاله:
یادگیری تقویتی عمیق برای برنامه ریزی نگهداری مبتنی بر شرایط سیستم های چند جزئی تحت خطرات رقابت وابسته
منبع:
Sciencedirect - Elsevier - Reliability Engineering and System Safety, 203 (2020) 107094. doi:10.1016/j.ress.2020.107094
نویسنده:
Nailong Zhanga,⁎, Wujun Sib
چکیده انگلیسی:
Condition-Based Maintenance (CBM) planning for multi-component systems has been receiving increasing attention
in recent years. Most existing research on CBM assumes that preventive maintenances should be conducted
when the degradations of system components reach specific threshold levels upon inspection. However,
the search of optimal maintenance threshold levels is often efficient for low-dimensional CBM but becomes
challenging if the number of components gets large, especially when those components are subject to complex
dependencies. To overcome the challenge, in this paper we propose a novel and flexible CBM model based on a
customized deep reinforcement learning for multi-component systems with dependent competing risks. Both
stochastic and economic dependencies among the components are considered. Specifically, different from the
threshold-based decision making paradigm used in traditional CBM, the proposed model directly maps the multicomponent
degradation measurements at each inspection epoch to the maintenance decision space with a cost
minimization objective, and the leverage of deep reinforcement learning enables high computational efficiencies
and thus makes the proposed model suitable for both low and high dimensional CBM. Various numerical studies
are conducted for model validations.
Keywords: Maintenance | Markov decision process | Deep Q network | Failure dependency | Cost minimization
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0