An Adaptive Multivariate EWMA Control Chart for Monitoring Missing Data

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.