In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily re...In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.展开更多
The Extended Exponentially Weighted Moving Average(extended EWMA)control chart is one of the control charts and can be used to quickly detect a small shift.The performance of control charts can be evaluated with the a...The Extended Exponentially Weighted Moving Average(extended EWMA)control chart is one of the control charts and can be used to quickly detect a small shift.The performance of control charts can be evaluated with the average run length(ARL).Due to the deriving explicit formulas for the ARL on a two-sided extended EWMA control chart for trend autoregressive or trend AR(p)model has not been reported previously.The aim of this study is to derive the explicit formulas for the ARL on a two-sided extended EWMA con-trol chart for the trend AR(p)model as well as the trend AR(1)and trend AR(2)models with exponential white noise.The analytical solution accuracy was obtained with the extended EWMA control chart and was compared to the numer-ical integral equation(NIE)method.The results show that the ARL obtained by the explicit formula and the NIE method is hardly different,but the explicit for-mula can help decrease the computational(CPU)time.Furthermore,this is also expanded to comparative performance with the Exponentially Weighted Moving Average(EWMA)control chart.The performance of the extended EWMA control chart is better than the EWMA control chart for all situations,both the trend AR(1)and trend AR(2)models.Finally,the analytical solution of ARL is applied to real-world data in the healthfield,such as COVID-19 data in the United Kingdom and Sweden,to demonstrate the efficacy of the proposed method.展开更多
Control charts are one of the tools in statistical process control widely used for monitoring,measuring,controlling,improving the quality,and detecting problems in processes in variousfields.The average run length(ARL)...Control charts are one of the tools in statistical process control widely used for monitoring,measuring,controlling,improving the quality,and detecting problems in processes in variousfields.The average run length(ARL)can be used to determine the efficacy of a control chart.In this study,we develop a new modified exponentially weighted moving average(EWMA)control chart and derive explicit formulas for both one and the two-sided ARLs for a p-order autoregressive(AR(p))process with exponential white noise on the new modified EWMA control chart.The accuracy of the explicit formulas was compared to that of the well-known numerical integral equation(NIE)method.Although both methods were highly consistent with an absolute percentage difference of less than 0.00001%,the ARL using the explicit formulas method could be computed much more quickly.Moreover,the performance of the explicit formulas for the ARL on the new modified EWMA control chart was better than on the modified and standard EWMA control charts based on the relative mean index(RMI).In addition,to illustrate the applicability of using the proposed explicit formulas for the ARL on the new modified EWMA control chart in practice,the explicit formulas for the ARL were also applied to a process with real data from the energy and agriculturalfields.展开更多
利用Burr分布来近似各种非正态分布对非正态情形下的EWM A均值控制图进行可变抽样区间设计,采用M arkov cha in方法计算过程的平均报警时间,数据结果显示,所设计的控制图较常规的固定抽样区间控制图可能够缩短过程失控时间从而提高控制...利用Burr分布来近似各种非正态分布对非正态情形下的EWM A均值控制图进行可变抽样区间设计,采用M arkov cha in方法计算过程的平均报警时间,数据结果显示,所设计的控制图较常规的固定抽样区间控制图可能够缩短过程失控时间从而提高控制图的效率。展开更多
基金This research was funded by the Basic Research Funds for Universities in Inner Mongolia Autonomous Region(No.JY20220272)the Scientific Research Program of Higher Education in InnerMongolia Autonomous Region(No.NJZZ23080)+3 种基金the Natural Science Foundation of InnerMongolia(No.2023LHMS05054)the NationalNatural Science Foundation of China(No.52176212)We are also very grateful to the Program for Innovative Research Team in Universities of InnerMongolia Autonomous Region(No.NMGIRT2213)The Central Guidance for Local Scientific and Technological Development Funding Projects(No.2022ZY0113).
文摘In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.
基金Thailand Science ResearchInnovation Fund,and King Mongkut's University of Technology North Bangkok Contract No.KMUTNB-FF-65-45.
文摘The Extended Exponentially Weighted Moving Average(extended EWMA)control chart is one of the control charts and can be used to quickly detect a small shift.The performance of control charts can be evaluated with the average run length(ARL).Due to the deriving explicit formulas for the ARL on a two-sided extended EWMA control chart for trend autoregressive or trend AR(p)model has not been reported previously.The aim of this study is to derive the explicit formulas for the ARL on a two-sided extended EWMA con-trol chart for the trend AR(p)model as well as the trend AR(1)and trend AR(2)models with exponential white noise.The analytical solution accuracy was obtained with the extended EWMA control chart and was compared to the numer-ical integral equation(NIE)method.The results show that the ARL obtained by the explicit formula and the NIE method is hardly different,but the explicit for-mula can help decrease the computational(CPU)time.Furthermore,this is also expanded to comparative performance with the Exponentially Weighted Moving Average(EWMA)control chart.The performance of the extended EWMA control chart is better than the EWMA control chart for all situations,both the trend AR(1)and trend AR(2)models.Finally,the analytical solution of ARL is applied to real-world data in the healthfield,such as COVID-19 data in the United Kingdom and Sweden,to demonstrate the efficacy of the proposed method.
基金Thailand Science Research and Innovation Fund,and King Mongkut’s University of Technology North Bangkok Contract no.KMUTNB-FF-65–45.
文摘Control charts are one of the tools in statistical process control widely used for monitoring,measuring,controlling,improving the quality,and detecting problems in processes in variousfields.The average run length(ARL)can be used to determine the efficacy of a control chart.In this study,we develop a new modified exponentially weighted moving average(EWMA)control chart and derive explicit formulas for both one and the two-sided ARLs for a p-order autoregressive(AR(p))process with exponential white noise on the new modified EWMA control chart.The accuracy of the explicit formulas was compared to that of the well-known numerical integral equation(NIE)method.Although both methods were highly consistent with an absolute percentage difference of less than 0.00001%,the ARL using the explicit formulas method could be computed much more quickly.Moreover,the performance of the explicit formulas for the ARL on the new modified EWMA control chart was better than on the modified and standard EWMA control charts based on the relative mean index(RMI).In addition,to illustrate the applicability of using the proposed explicit formulas for the ARL on the new modified EWMA control chart in practice,the explicit formulas for the ARL were also applied to a process with real data from the energy and agriculturalfields.
