An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring

• Activity Number: 329
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Technometrics
• Author(s): Kaibo Liu* and Yajun Mei and Jianjun Shi
• Companies: University of Wisconsin - Madison and Georgia Institute of Technology and Georgia Institute of Technology
• Keywords: Multiple data streams ; Partial information over the spatial domain ; Sum of the top-r local statistics ; Shift detection ; Sensor redeployment

Abstract

Temporally and spatially dense data-rich environments provide unprecedented opportunities and challenges for effective process control. In this talk, 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. 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. 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.