The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are d...The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.展开更多
As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was i...As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA (MEWMA) control charts, in which the optimal parameter pair ( λ ,k) or ( λ ,h) was searched by using the generalized regression neural network (GRNN). The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length (ARL0) and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.展开更多
传统Shewhart-p控制图只对单一属性的不合格品率进行监控,在过程发生偏移时有一定的滞后性。为提高不合格品率控制图的精度,提出一种多元指数加权移动平均不合格品率(multivariate exponentially weighted moving average p, MEWMA-p)...传统Shewhart-p控制图只对单一属性的不合格品率进行监控,在过程发生偏移时有一定的滞后性。为提高不合格品率控制图的精度,提出一种多元指数加权移动平均不合格品率(multivariate exponentially weighted moving average p, MEWMA-p)控制图。该控制图将多个属性的不合格品率应用于多元指数加权移动平均控制图,可同时对多个属性进行监控,并且对于小范围的偏移更加敏感。对比分析同等偏移程度下指数加权移动平均不合格品率(exponentially weighted moving average p, EWMA-p)控制图与MEWMA-p控制图的平均运行长度(average run length,ARL)结果,并通过模拟仿真说明该方法的有效性。展开更多
为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved ...为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved sparrow search algorithm optimized deep belief network,简称FISSA-DBN)的风电机组状态监测新方法。首先,对SCADA数据进行预处理分析,并利用专家系统和皮尔逊相关系数分析,相关分析选取输入参数和输出参数;其次,利用预处理数据集建立基于FISSA-DBN的风电机组运行状态监测新模型,根据模型预测值和实际输出值之间的重构值误差,以及指数加权移动平均阈值(exponentially weighted moving average,简称EWMA)判断是否有异常;最后,以华东某风电场实际数据为例进行实例验证。结果表明,所提出方法的预警时间比实际记录时间最早可提前4 d多。同时,将所提出方法与其他方法进行对比,结果表明该方法预警时间提前,模型预测误差更小。展开更多
主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,...主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA–PCA)方法用于解决PCA不能有效检出微小故障的问题.本文深入研究了MEWMA–PCA中EWMA影响主元分析进行故障检测的机制,导出了MEWMA–PCA可检出微小故障的原因.本文确定了MEWMA–PCA中遗忘因子λ、单传感器故障幅值和迟延时间三者的关系,并进行了数值仿真和火电厂磨煤机组运行状态的仿真实验.实验结果验证了MEWMA–PCA中EWMA提高PCA的监测性能的机制,并给出了根据系统实际要求来选取合适的遗忘因子值,从而在规定的时间内检出微小故障的实例.展开更多
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,JeddahThe authors,therefore,gratefully acknowledge the DSR technical and financial support.
文摘The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.
基金Funded by the National Key Technologies R&D Programs of China (No.2002BA105C)
文摘As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA (MEWMA) control charts, in which the optimal parameter pair ( λ ,k) or ( λ ,h) was searched by using the generalized regression neural network (GRNN). The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length (ARL0) and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.
文摘传统Shewhart-p控制图只对单一属性的不合格品率进行监控,在过程发生偏移时有一定的滞后性。为提高不合格品率控制图的精度,提出一种多元指数加权移动平均不合格品率(multivariate exponentially weighted moving average p, MEWMA-p)控制图。该控制图将多个属性的不合格品率应用于多元指数加权移动平均控制图,可同时对多个属性进行监控,并且对于小范围的偏移更加敏感。对比分析同等偏移程度下指数加权移动平均不合格品率(exponentially weighted moving average p, EWMA-p)控制图与MEWMA-p控制图的平均运行长度(average run length,ARL)结果,并通过模拟仿真说明该方法的有效性。
文摘为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved sparrow search algorithm optimized deep belief network,简称FISSA-DBN)的风电机组状态监测新方法。首先,对SCADA数据进行预处理分析,并利用专家系统和皮尔逊相关系数分析,相关分析选取输入参数和输出参数;其次,利用预处理数据集建立基于FISSA-DBN的风电机组运行状态监测新模型,根据模型预测值和实际输出值之间的重构值误差,以及指数加权移动平均阈值(exponentially weighted moving average,简称EWMA)判断是否有异常;最后,以华东某风电场实际数据为例进行实例验证。结果表明,所提出方法的预警时间比实际记录时间最早可提前4 d多。同时,将所提出方法与其他方法进行对比,结果表明该方法预警时间提前,模型预测误差更小。
文摘主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA–PCA)方法用于解决PCA不能有效检出微小故障的问题.本文深入研究了MEWMA–PCA中EWMA影响主元分析进行故障检测的机制,导出了MEWMA–PCA可检出微小故障的原因.本文确定了MEWMA–PCA中遗忘因子λ、单传感器故障幅值和迟延时间三者的关系,并进行了数值仿真和火电厂磨煤机组运行状态的仿真实验.实验结果验证了MEWMA–PCA中EWMA提高PCA的监测性能的机制,并给出了根据系统实际要求来选取合适的遗忘因子值,从而在规定的时间内检出微小故障的实例.
