Jay Lee Receives SM Wu Research Implementation Award

Professor Jay Lee has received the 2022 SME SM Wu Research Implementation Award.

Of the many awards Professor Lee has received, this is one of the most personal to him. Professor Lee studied under Professor SM Wu as a graduate student. In his acceptance speech, Professor Lee shared a story about one of his interactions with Dr. Wu:

"At the 1992 NAMRC Conference at Washington DC, My last discussion with Dr. Wu was about my passion and completed research in Machine Degradation. Dr. Wu said that this was a great field and that the main challenges will be how to implement into industry. When I became a Professor in 2000, the implementation of this research began with the establishment of the NSF I/UCRC for Intelligent Maintenance Systems, which sought to translate cutting-edge research into implementable technologies for transitioning industry from a fail-and-fix, to a predict-and-prevent methodology. It is such and honor to receive this implementation award. I hope that today Dr. Wu is proud of this accomplishment. I also want to especially thank our members for their continued support throughout our journey: Applied Materials, Foxconn, GE, Harley Davidson, Hitachi, Mazak, National Instruments, Nissan, OMRON, P&G, and Toyota, and others."

Please join the IMS Center in congratulating Professor Lee on this great accomplishment.


From SME

Dr. Lee has frequently contributed high-impact research works to SME NAMRC Conferences and technical journals for publication. The four selected NAMRC papers are listed below. In the 2003 NAMRC paper, Dr. Lee and his collaborators proposed a novel algorithm for generic process/machine performance assessment based on merged multiple sensor readings. This algorithm is built on the extraction of generic signal features and generic methods of signature matching, and thus the methodology is application-independent, and can be applied in a wide range of applications. The concept was quickly adopted in many applications by researchers in the United States, Japan, Spain, New Zealand, and China. In the three NAMRC papers published in 2019-202, Dr. Lee and his collaborators developed advanced machine learning techniques such as deep learning and domain adaptation for prognostics and diagnosis in industrial applications.

About this Award

The NAMRI | SME S.M. Wu Research Implementation Award, presented by NAMRI | SME, recognizes outstanding original research presented as a paper at the annual North American Manufacturing Research Conference (NAMRC) and subsequently, upon implementation, had a significant commercial and/or societal impact.

The award is in honor of Shien-Ming Wu (1924-92), PhD, FSME, the J. Reid and Polly Anderson Professor of Manufacturing Technology, University of Michigan, Ann Arbor. An internationally known researcher in the fields of manufacturing engineering and dynamic systems analysis, Wu created and defined the modern field of manufacturing automation. He was the first researcher to introduce advanced statistical techniques to manufacturing research. Called the dynamic data system, Wu's methodology provides a mathematical description of complex manufacturing processes based on online system operational data that can be used for system diagnostics and quality control. The dynamic data system is the basis for quality improvement programs implemented by manufacturing firms worldwide, including General Motors Corp., Ford Motor Co. and Chrysler Corp.

You can learn more about this award here.


To learn more about Professor Lee's contributions to NAMRI & SME, please see below number of Professor Lee's publications that speak to his ability to translate research into implementable technologies that can drive real-world impact in industry.

  • “Multisensor process performance assessment through the use of autoregressive modeling and feature maps”, Transactions of XXXI SME/NAMRI, 2003, 31.483-490.
  • “Detection and diagnosis of bottle capping failures based on motor current signature analysis,” Procedia Manufacturing 2019 34, 840-846
  • “Deep learning-based intelligent process monitoring of directed energy deposition in additive manufacturing with thermal images,” Procedia Manufacturing 2020, 48, 643-649
  • “Enhancing intelligent cross-domain fault diagnosis performance on rotating machines with noisy health labels,” Procedia Manufacturing 2020, 48, 940-946 


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