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Multi-component opportunistic maintenance optimization for wind turbines with consideration of seasonal factor

Journal of Central South University 2020年第2期27卷 490-499页

作者： SU Chun HU Zhao-yong LIU Yang School of Mechanical Engineering Southeast University

Aiming at wind turbines,the opportunistic maintenance optimization is carried out for multi-component system,where minimal repair,imperfect repair,replacement as well as their effects on component’s effective age are *** each inspection point,appropriate maintenance mode is selected according to the component’s effective age and its maintenance *** utilize the maintenance opportunities for the components among the wind turbines,opportunistic maintenance approach is ***,the influence of seasonal factor on the component’s failure rate and improvement factor’s decrease with the increase of repair’s times are also taken into *** maintenance threshold is set as the decision variable,and an opportunistic maintenance optimization model is proposed to minimize wind turbine’s life-cycle maintenance ***,genetic algorithm is adopted to solve the model,and the effectiveness is verified with a case *** results show that based on the component’s inherent reliability and maintainability,the proposed model can provide optimal maintenance plans ***,the higher the component’s reliability and maintainability are,the less the times of repair and replacement will be.

关键词： wind turbine reliability seasonal factor multi-component maintenance opportunistic maintenance

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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