Jan Shi Receives the 2019 Brumbaugh Award for Best ASQ Published Paper

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Summary Sentence:

The Brumbaugh Award is given to the paper making the largest single contribution to the development of industrial application of quality control.

Full Summary:

The Brumbaugh Award is given to the paper making the largest single contribution to the development of industrial application of quality control.

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  • Carolyn J. Stewart Chair and Professor Jan Shi Carolyn J. Stewart Chair and Professor Jan Shi
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Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering announced that Carolyn J. Stewart Chair and Professor Jianjun “Jan” Shi has been honored with the American Society for Quality’s (ASQ) 2019 Brumbaugh Award.

According to the ASQ, the Brumbaugh Award, which has been presented since 1949, is given to “the paper making the largest single contribution to the development of industrial application of quality control.” The chosen paper is selected from publications in the seven journals published by ASQ in the given year.

Shi’s award-winning paper, “Multiple Profiles Sensor-based Monitoring and Anomaly Detection,” is co-authored with Chen Zhang, Hao Yan, and Seungho Lee. Zhang, a visiting student in ISyE in 2016, is now an assistant professor at Tsinghua University. Yan, a former ISyE Ph.D. student under the supervision of Shi, is now an assistant professor in computer informatics decision systems engineering at Arizona State University. Lee is from Samsung Electronics.

“Congratulations to Jan on winning the 2019 ASQ Brumbaugh Award,” said ISyE School Chair Edwin Romeijn. “He is a pioneer in the development and application of data-enabled manufacturing, and as this award demonstrates, his collaborations and research in these areas continue to make a significant impact on his field.”

This paper targets to design a monitoring framework for advanced manufacturing systems, where hundreds of sensors produce high-dimensional streaming data collected in millisecond intervals. Modeling and monitoring these high-dimensional data streams is very challenging due to the complex sensor-to-sensor correlation structures and the sparse abnormal patterns. This paper presents a novel real-time multi-channel profile monitoring system for such processes and demonstrates an improved monitoring performance in the real manufacturing system. In the age of data revolution, developing efficient data fusion methods for complex multi-sensor systems will continue to be an important research area in the future.

"This research is motivated by real industrial needs of combining multiple sensors for effective process monitoring,” said Shi. “Toward this effort we analyzed sophisticated industrial data to design the monitoring system, and invented new methodologies to ensure its applicability and effectiveness in the real system.

“This award is a great acknowledgment for our research and recognition of our developed data fusion methods for complex multi-sensor systems, which makes a significant impact on both academic research and real manufacturing systems,” added Shi.

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE), The Supply Chain and Logistics Institute (SCL)

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Status
  • Created By: Shelley Wunder-Smith
  • Workflow Status: Published
  • Created On: Jan 18, 2019 - 1:00pm
  • Last Updated: Jan 24, 2019 - 3:10pm