针对两个正态随机变量比率(Ratio of Two Normal Random Variables,RZ)监控的研究是近年来统计过程控制的重要方向之一。为了进一步提高传统休哈特型RZ控制图对过程中较小或中等参数偏移的灵敏度,本文以指数加权移动平均(Exponentially ...针对两个正态随机变量比率(Ratio of Two Normal Random Variables,RZ)监控的研究是近年来统计过程控制的重要方向之一。为了进一步提高传统休哈特型RZ控制图对过程中较小或中等参数偏移的灵敏度,本文以指数加权移动平均(Exponentially Weighted Moving Average,EWMA) RZ控制图为基础,提出了一种新的RZ控制图。首先,对EWMA-RZ控制图的平滑系数进行两次加权,提出了二次指数加权移动平均(Double EWMA,DEWMA) RZ控制图,并进一步引入了变采样间隔(Variable Sampling Interval,VSI)特性,提出了VSI-DEWMA-RZ控制图;其次,采用蒙特卡罗(Monte-Carlo,MC)仿真模拟所提出控制图的运行链长分布特征,并详细分析了控制图的性能;再次,针对不同的控制图参数,比较了VSI-DEWMA-RZ控制图与DEWMA-RZ和VSI-EWMA-RZ控制图的性能。仿真结果表明,本文提出的VSI-DEWMA-RZ控制图优于DEWMA-RZ控制图,且其对过程中较小和中等偏移的监控效果优于现有的VSI-EWMA-RZ控制图。最后,通过监控食品加工过程中“南瓜籽”和“亚麻籽”的重量,进一步说明了所提出控制图的优越性。展开更多
This study aims to examine the explicit solution for calculating the Average Run Length(ARL)on the triple exponentially weighted moving average(TEWMA)control chart applied to autoregressive model(AR(p)),where AR(p)is ...This study aims to examine the explicit solution for calculating the Average Run Length(ARL)on the triple exponentially weighted moving average(TEWMA)control chart applied to autoregressive model(AR(p)),where AR(p)is an autoregressive model of order p,representing a time series with dependencies on its p previous values.Additionally,the study evaluates the accuracy of both explicit and numerical integral equation(NIE)solutions for AR(p)using the TEWMA control chart,focusing on the absolute percentage relative error.The results indicate that the explicit and approximate solutions are in close agreement.Furthermore,the study investigates the performance of exponentially weighted moving average(EWMA)and TEWMA control charts in detecting changes in the process,using the relative mean index(RMI)as a measure.The findings demonstrate that the TEWMA control chart outperforms the EWMA control chart in detecting process changes,especially when the value ofλis sufficiently large.In addition,an analysis using historical data from the SET index between January 2024 and May 2024 and historical data of global annual plastic production,the results of both data sets also emphasize the superior performance of the TEWMA control chart.展开更多
With the increasing complexity of production processes,there has been a growing focus on online algorithms within the domain of multivariate statistical process control(SPC).Nonetheless,conventional methods,based on t...With the increasing complexity of production processes,there has been a growing focus on online algorithms within the domain of multivariate statistical process control(SPC).Nonetheless,conventional methods,based on the assumption of complete data obtained at uniform time intervals,exhibit suboptimal performance in the presence of missing data.In our pursuit of maximizing available information,we propose an adaptive exponentially weighted moving average(EWMA)control chart employing a weighted imputation approach that leverages the relationships between complete and incomplete data.Specifically,we introduce two recovery methods:an improved K-Nearest Neighbors imputing value and the conventional univariate EWMA statistic.We then formulate an adaptive weighting function to amalgamate these methods,assigning a diminished weight to the EWMA statistic when the sample information suggests an increased likelihood of the process being out of control,and vice versa.The robustness and sensitivity of the proposed scheme are shown through simulation results and an illustrative example.展开更多
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
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
文摘针对两个正态随机变量比率(Ratio of Two Normal Random Variables,RZ)监控的研究是近年来统计过程控制的重要方向之一。为了进一步提高传统休哈特型RZ控制图对过程中较小或中等参数偏移的灵敏度,本文以指数加权移动平均(Exponentially Weighted Moving Average,EWMA) RZ控制图为基础,提出了一种新的RZ控制图。首先,对EWMA-RZ控制图的平滑系数进行两次加权,提出了二次指数加权移动平均(Double EWMA,DEWMA) RZ控制图,并进一步引入了变采样间隔(Variable Sampling Interval,VSI)特性,提出了VSI-DEWMA-RZ控制图;其次,采用蒙特卡罗(Monte-Carlo,MC)仿真模拟所提出控制图的运行链长分布特征,并详细分析了控制图的性能;再次,针对不同的控制图参数,比较了VSI-DEWMA-RZ控制图与DEWMA-RZ和VSI-EWMA-RZ控制图的性能。仿真结果表明,本文提出的VSI-DEWMA-RZ控制图优于DEWMA-RZ控制图,且其对过程中较小和中等偏移的监控效果优于现有的VSI-EWMA-RZ控制图。最后,通过监控食品加工过程中“南瓜籽”和“亚麻籽”的重量,进一步说明了所提出控制图的优越性。
基金the National Science,Research and Innovation Fund(NSRF)King Mongkuts University of Technology North Bangkok under contract no.KMUTNB-FF-68-B-08.
文摘This study aims to examine the explicit solution for calculating the Average Run Length(ARL)on the triple exponentially weighted moving average(TEWMA)control chart applied to autoregressive model(AR(p)),where AR(p)is an autoregressive model of order p,representing a time series with dependencies on its p previous values.Additionally,the study evaluates the accuracy of both explicit and numerical integral equation(NIE)solutions for AR(p)using the TEWMA control chart,focusing on the absolute percentage relative error.The results indicate that the explicit and approximate solutions are in close agreement.Furthermore,the study investigates the performance of exponentially weighted moving average(EWMA)and TEWMA control charts in detecting changes in the process,using the relative mean index(RMI)as a measure.The findings demonstrate that the TEWMA control chart outperforms the EWMA control chart in detecting process changes,especially when the value ofλis sufficiently large.In addition,an analysis using historical data from the SET index between January 2024 and May 2024 and historical data of global annual plastic production,the results of both data sets also emphasize the superior performance of the TEWMA control chart.
文摘With the increasing complexity of production processes,there has been a growing focus on online algorithms within the domain of multivariate statistical process control(SPC).Nonetheless,conventional methods,based on the assumption of complete data obtained at uniform time intervals,exhibit suboptimal performance in the presence of missing data.In our pursuit of maximizing available information,we propose an adaptive exponentially weighted moving average(EWMA)control chart employing a weighted imputation approach that leverages the relationships between complete and incomplete data.Specifically,we introduce two recovery methods:an improved K-Nearest Neighbors imputing value and the conventional univariate EWMA statistic.We then formulate an adaptive weighting function to amalgamate these methods,assigning a diminished weight to the EWMA statistic when the sample information suggests an increased likelihood of the process being out of control,and vice versa.The robustness and sensitivity of the proposed scheme are shown through simulation results and an illustrative example.
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.
