Monitoring Multivariate Data via KNN Learning

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they either cannot deliver satisfactory detection performance or cannot cope with massive amounts of complex data. In this paper, we propose a novel k-nearest neighbors empirical cumulative sum (KNN-ECUSUM) control chart for monitoring multivariate data by utilizing historical data under in-control and out-of-control scenarios. Our proposed method utilizes the k-nearest neighbors (KNN) algorithm for dimension reduction to transform multi-attribute data into univariate data, and then applies the CUSUM procedure to monitor the change in the empirical distributions of the transformed univariate data. Simulation studies and a real industrial example based on a disk monitoring system demonstrates the effectiveness of our proposed method.