An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring

Temporally and spatially dense data-rich environments provide unprecedented opportunities and challenges for effective process control. In this article, we propose a systematic and scalable adaptive sampling strategy for online high-dimensional process monitoring in the context of limited resources with only partial information available at each acquisition time. The proposed adaptive sampling strategy includes a broad range of applications: (1) when only a limited number of sensors is available; (2) when only a limited number of sensors can be in "ON" state in a fully deployed sensor network; and (3) when only partial data streams can be analyzed at the fusion center due to limited transmission and processing capabilities even though the full data streams have been acquired remotely. A monitoring scheme of using the sum of top-r local CUSUM statistics is developed and named as "TRAS" (top-r based adaptive sampling), which is scalable and robust in detecting a wide range of possible mean shifts in all directions, when each data stream follows a univariate normal distribution. Two properties of this proposed method are also investigated. Case studies are performed on a hot-forming process and a real solar flare process to illustrate and evaluate the performance of the proposed method.