A sensitized variable selection control chart based on a classification algorithm for monitoring high‐dimensional processes

Abstract

AbstractDuring the last decade, variable‐selection‐based (VS) control charts have gained much popularity for process monitoring and diagnosis. These charts have been proven efficient for the detection of sparse mean shifts in high‐dimensional processes. VS charts usually assume that in‐control (IC) data are the only information used to determine the control limits. In modern industrial processes, however, out‐of‐control (OC) data can be easily collected. Detecting a specific shift in a data‐rich environment without utilizing OC data information will limit the development of a process monitoring scheme. In this paper, a novel variable selection control chart that is combined with a classification algorithm is proposed, which is expected to benefit from both the classification and variable selection approaches. In contrast to alternative charts, the proposed sensitized variable selection chart can capture the potential shifted variables using both IC and OC information, which can improve the sensitivity of the chart in a specific direction. Extensive Monte Carlo simulations demonstrate that the proposed chart outperforms the alternatives in a data‐rich and high‐dimensional environment. A real‐life example of cellular localization is also included to support the findings of our study.