Collaborative Research: Online Monitoring of High-Dimensional Streaming Data Using Adaptive Order Shrinkage

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Modern manufacturing machines and systems incorporate sensors to monitor process conditions, but the massive amount of data coming from them is often difficult to interpret. This project investigates a new method for extracting useful information from such data. The method potentially can reduce computational cost and improve confidence in the predictions that are made by adapting statistical process control (SPC) methodologies specifically for online monitoring of high-dimensional streaming data. While this work focuses on monitoring the Chemical Mechanical Planarization (CMP) process, the methods can also be applied to other manufacturing applications, such as rolling, forging and casting processes. The impacts of the methodologies will go beyond manufacturing, including but not limited to disease surveillance in epidemiology, network traffic control, intrusion detection and surveillance video. The success of the implementation of the research methods will not only generate significant economic impacts to the nation, but also prevent consequent damages through quick detection of abnormal events. In addition, the education plan will make broad impacts on the workforce training through curriculum and lab developments, teaching innovations, and other outreach activities.The objective of this collaborative research is to develop scalable and adaptive methodologies for online monitoring of high-dimensional streaming data. In particular, three interrelated research tasks are planned in the methodology development: (1) Efficient scalable schemes via adaptive order shrinkage with full observations, and the key novel idea is to first monitor each data stream locally through some classical, computationally simple, but efficient local detection statistics, and then combine these local procedures ?smartly? to produce a single global monitoring scheme; (2) Adaptive sampling strategies over the spatial domain, rather than the conventional time domain, such that the most informative data streams are actively selected/sampled to maximize the sensitivity and effectiveness for change detection with consideration of resources constraints; and, (3) The engineering knowledge enhanced monitoring scheme that integrates domain knowledge with local detection statistics development and adaptive sampling strategy to further improve performance. The success of this research will advance the state of the art in statistical process control and contribute to the science base of quality improvement for manufacturing systems.