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Data Driven Analytics (Machine Learning) for System Characterization, Diagnostics and Control Optimization

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Book cover Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

This presentation discusses the potential use of machine learning techniques to build data-driven models to characterize an engineering system for performance assessment, diagnostic analysis and control optimization. Focusing on the Gaussian Process modeling approach, engineering applications on constructing predictive models for energy consumption analysis and tool condition monitoring of a milling machine tool are presented. Furthermore, a cooperative control optimization approach for maximizing wind farm power production by combining Gaussian Process modeling with Bayesian Optimization is discussed.

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Acknowledgments and Disclaimer

The authors would like to acknowledge the assistance from the late Prof. David Dornfeld of the Laboratory for Manufacturing and Sustainability at UC Berkeley and Dr. Raunak Bhinge of Infinite Uptime, Inc., who conducted the experiments and collected the machine operation data on the Mori Seiki Milling Machine. The authors would also like to thank Prof. Soon-Duck Kwon of Chonbuk National University in Korea, who generously provided the wind tunnel laboratory (KOCED Wind Tunnel Center) facilities for the wind farm power production experiments.

The work described in this paper was partially supported by the National Institute of Standards and Technology (NIST) cooperative agreement with Stanford University (Grant No. 70NANB12H273 and 70NANB17H031), and the US National Science Foundation (NSF) (Grant No. ECCS-1446330). Any opinions, findings, conclusions or recommendations expressed in the paper are solely those of the authors and do not necessary reflect the views of NSF, NIST and the authors’ collaborators. Certain commercial systems are identified in this paper; such identification does not imply recommendation or endorsement by NSF, NIST, Stanford University or the authors; nor does it imply that the products identified are necessarily the best available for the purpose.

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Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

    Jinkyoo Park

  2. Stanford University, Stanford, CA, 94305, USA

    Max Ferguson & Kincho H. Law

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  1. Jinkyoo Park
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  2. Max Ferguson
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  3. Kincho H. Law
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Correspondence to Kincho H. Law .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Park, J., Ferguson, M., Law, K.H. (2018). Data Driven Analytics (Machine Learning) for System Characterization, Diagnostics and Control Optimization. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_2

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