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 im-proved K-Nearest Neighbors imputing value and the conventional univariate EWMA statistic.We then formulate an adaptive weighting function to amalgamate these methods,assigning a dimin-ished 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.
关键词
在线监控/完全随机缺失/加权插补/指数加权移动平均/改进的K近邻
Key words
online monitoring/completely random missing/weighted imputing values/EWMA/improved K-nearest neighbors
维普
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