基于系统动力学的风力机备件需求预测研究
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摘要
考虑到服役时间、备件寿命、维修策略及环境4种因素对备件需求发生的影响,利用系统动力学建立风力机备件需求预测模型。模型主要由服役时间仿真模块、备件可靠度和故障率仿真模块及维修方式仿真模块组成。仿真结果表明:该模型能体现出风力机备件需求的特点,同时在边界条件内预测需求发生时刻,为备件管理者提供备货信息。
Considering the influence of service time,spare parts life,maintenance strategy and environment on the demand of spare parts,the System Dynamic model is used to establish the forecast model of spare parts demand of the wind turbine. The model consists of service time simulation module,spare parts reliability and failure rate simulation module, maintenance mode simulation module. The simulation results showed that the model can reflect the characteristics of the demand of the spare parts of the wind turbine and forecast the time of occurrence of the demand in the boundary condition,and provide the stocking information for the spare parts manager.
Considering the influence of service time,spare parts life,maintenance strategy and environment on the demand of spare parts,the System Dynamic model is used to establish the forecast model of spare parts demand of the wind turbine. The model consists of service time simulation module,spare parts reliability and failure rate simulation module, maintenance mode simulation module. The simulation results showed that the model can reflect the characteristics of the demand of the spare parts of the wind turbine and forecast the time of occurrence of the demand in the boundary condition,and provide the stocking information for the spare parts manager.
引文
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[2]于强强.风力发电机组备品备件编码的设计与实现[D].北京:华北电力大学,2015.[2]Yu Qiangqiang.Design and implementation of spare parts coding for wind turbines[D].Beijing:North China Electric Power University,2015.
[3]张国辉.风力发电场备品备件管理分析[A].中国农业机械工业协会风能设备分会.第二届中国风电后市场专题研讨会论文集[C].中国农业机械工业协会风能设备分会,呼和浩特,2015.[3]Zhang Guohui.Analysis of wind power equipment spare parts management of wind farm[A].China Wind Power Equipment Branch of Agricultural Machinery Industry Association.The second session of China Wind Power Market Seminar Proceedings,China Agricultural Machinery Industry Association of wind energy equipment branch[C],Huhhot,2015.
[4]Croston J D.Forecasting and stock control for intermittent demands[J].Operational Research Quarterly,1972,23(3):289-303.
[5]Johnston F R,Boylan J E.Forecasting intermittent demand:A comparative evaluation of croton’s method.Comment[J].International Journal of Forecasting,2004,12(2):297-298.
[6]Levén E,Segerstedt A.Inventory control with a modified Croston procedure and Erlang distribution[J].International Journal of Production Economics,2004,90(3):361-367.
[7]Efron B.Bootstrap methods:Another look at the jackknife[J].Annals of Statistics,1979,7(1):1-26.
[8]Snyder R.Forecasting sales of slow and fast moving inventories[J].European Journal of Operational Research,2002,140(3):684-699.
[9]Willemain T R,Smart C N,Schwarz H F.A new approach to forecasting intermittent demand for service parts inventories[J].International Journal of Forecasting,2004,20(3):375-387.
[10]赵建忠,徐廷学,李海军,等.基于Logistic回归、Markov过程和改进灰自助法的导弹备件需求预测[J].科技导报,2013,16:51-55.[10]Zhao Jianzhong,Xu Tingxue,Li Haijun,et al.Prediction of missile spare parts demand based on Logistic regression,Markov process and improved grey self-help method[J].Technology Guide,2013,16:51-55.
[11]林琳,陈湘芝,钟诗胜.一种新的间断型备件需求预测方法[J].哈尔滨工业大学学报,2016,48(1):40-45.[11]LinLin,Chen Xiangzhi,Zhong Shisheng.A new approach of forecasting intermittent demand for spare parts[J].Journal of Harbin Institute of Technology,2016,48(1):40-45.
[12]杨杰,张斌,华中生.间断需求预测方法综述[J].预测,2005,24(5):70-75.[12]Yang Jie,Zhang Bin,Hua Zhongsheng.Review of intermittent demand forecasting methods[J].Forecast,2005,24(5):70-75.
[13]钟永光,贾晓菁,钱颖,等.系统动力学[M].北京:科学出版社,2009.[13]Zhong Yongguang,Jia Xiaojing,Qian Ying,et al.System Dynamics[M].Beijing:Science Press,2009.
[14]卓明良,潘尔顺,廖雯竹,等.基于可靠度限制的周期性预防维护模型研究[J].工业工程与管理,2009,(1):62-65.[14]Zhuo Mingliang,Pan Ershun,Liao Wenzhu,et al.Study on periodic preventive maintenance model based on limitation of reliability[J].Industrial Engineering and Management,2009,(1):62-65.