Wind turbine maintenance optimization faces challenges in balancing economic efficiency with operational reliability under environmental uncertainty.Traditional maintenance approaches exhibit limitations in adaptive d...Wind turbine maintenance optimization faces challenges in balancing economic efficiency with operational reliability under environmental uncertainty.Traditional maintenance approaches exhibit limitations in adaptive decision-making,leading to increased operational costs and reliability risks.This study develops a physicsinformed reinforcement learning framework that integrates established domain knowledge with adaptive deci-sion algorithms.The approach embeds physical principles-including Weibull wind dynamics and multi-stage degradation models-into a reinforcement learning architecture,while introducing bidirectional temperature-degradation coupling for enhanced failure prediction.A high-fidelity simulation environment enables policy training through Proximal Policy Optimization,capturing complex interactions between environmental vari-ability and equipment deterioration.The framework was validated through case study implementation using northern China wind farm operational data.Results demonstrate zero-failure operation over simulated 19-year lifecycles,with economic performance improvements of 109.3%and 54.5%compared to conventional periodic and threshold-based maintenance strategies.By integrating physical constraints with intelligent algorithms,the method achieves adaptive maintenance decisions based on multi-dimensional state information.展开更多
Traditional modular design methods lead to product maintenance problems, because the module form of a system is created according to either the function requirements or the manufacturing considerations. For solving th...Traditional modular design methods lead to product maintenance problems, because the module form of a system is created according to either the function requirements or the manufacturing considerations. For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the maintenance related ones. First, modularity parameters and modularity scenarios for product modularity are defined. Then the reliability and economic assessment models of product modularity strategies are formulated with the introduction of the effective working age of modules. A mathematical model used to evaluate the difference among the modules of the product so that the optimal module of the product can be established. After that, a multi-objective optimization problem based on metrics for preventive maintenance interval different degrees and preventive maintenance economics is formulated for modular optimization. Multi-objective GA is utilized to rapidly approximate the Pareto set of optimal modularity strategy trade-offs between preventive maintenance cost and preventive maintenance interval difference degree. Finally, a coordinate CNC boring machine is adopted to depict the process of product modularity. In addition, two factorial design experiments based on the modularity parameters are constructed and analyzed. These experiments investigate the impacts of these parameters on the optimal modularity strategies and the structure of module. The research proposes a new modular design method, which may help to improve the maintainability of product in modular design.展开更多
基金supported by the National Natural Science Foundation of China(Grant No 51767017)Gansu Province Basic Research Innovation Group Project(Grant No 18JR3RA133)+3 种基金Gansu Province Higher Education Industry Support and Guidance Project(Grant No 2022CYZC-22)Gansu Province Department of Ed-ucation Graduate Student’Innovation Star’Project(Grant No 2025CXZX-497)the Gansu Province Outstanding Doctoral Student Project(Grant No 25JRRA115)the Gansu Province Joint Research Foundation Major Program(Grant No 25JRRA1143).
文摘Wind turbine maintenance optimization faces challenges in balancing economic efficiency with operational reliability under environmental uncertainty.Traditional maintenance approaches exhibit limitations in adaptive decision-making,leading to increased operational costs and reliability risks.This study develops a physicsinformed reinforcement learning framework that integrates established domain knowledge with adaptive deci-sion algorithms.The approach embeds physical principles-including Weibull wind dynamics and multi-stage degradation models-into a reinforcement learning architecture,while introducing bidirectional temperature-degradation coupling for enhanced failure prediction.A high-fidelity simulation environment enables policy training through Proximal Policy Optimization,capturing complex interactions between environmental vari-ability and equipment deterioration.The framework was validated through case study implementation using northern China wind farm operational data.Results demonstrate zero-failure operation over simulated 19-year lifecycles,with economic performance improvements of 109.3%and 54.5%compared to conventional periodic and threshold-based maintenance strategies.By integrating physical constraints with intelligent algorithms,the method achieves adaptive maintenance decisions based on multi-dimensional state information.
基金Supported by National Natural Science Foundation of China(Grant Nos.51205347,51322506)Zhejiang Provincial Natural Science Foundation of China(Grant No.LR14E050003)+3 种基金Project of National Science and Technology Plan of China(Grant No.2013IM030500)Fundamental Research Funds for the Central Universities of ChinaInnovation Foundation of the State Key Laboratory of Fluid Power Transmission and Control of ChinaZhejiang University K.P.Chao’s High Technology Development Foundation of China
文摘Traditional modular design methods lead to product maintenance problems, because the module form of a system is created according to either the function requirements or the manufacturing considerations. For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the maintenance related ones. First, modularity parameters and modularity scenarios for product modularity are defined. Then the reliability and economic assessment models of product modularity strategies are formulated with the introduction of the effective working age of modules. A mathematical model used to evaluate the difference among the modules of the product so that the optimal module of the product can be established. After that, a multi-objective optimization problem based on metrics for preventive maintenance interval different degrees and preventive maintenance economics is formulated for modular optimization. Multi-objective GA is utilized to rapidly approximate the Pareto set of optimal modularity strategy trade-offs between preventive maintenance cost and preventive maintenance interval difference degree. Finally, a coordinate CNC boring machine is adopted to depict the process of product modularity. In addition, two factorial design experiments based on the modularity parameters are constructed and analyzed. These experiments investigate the impacts of these parameters on the optimal modularity strategies and the structure of module. The research proposes a new modular design method, which may help to improve the maintainability of product in modular design.
