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Machine health management in smart factory: A review

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Abstract

In this paper, we present a review of machine health managements for the smart factory. As the Industry 4.0 leads current factory automation and intelligent machines, the machine health management for diagnostic and prognostic purposes are essential, and their importance is getting more significant for the realization of the smart factory in the Industry 4.0. After brief introductions to important concepts and definitions composing smart factory and Industry 4.0, the developments in maintenance strategies towards Prognostics and health management (PHM) of machines are summarized. The review of machine health managements is followed, classifying the references by the monitoring components, types of measurements, as well as PHM tools and algorithms. 94 existing articles are reviewed and summarized in this regard. The implementations of machine health managements within the smart factory are discussed in terms of data connectivity, communications, Cyber-physical system (CPS) and virtual factory, relating them to Internet of things (IoT), cloud computing, and big data management.

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Authors and Affiliations

  1. Institute of Advanced Machines and Design (IAMD), Seoul National University, Seoul, 08826, Korea

    Gil-Yong Lee, Won-Shik Chu & Sung-Hoon Ahn

  2. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 08826, Korea

    Mincheol Kim, Ying-Jun Quan, Min-Sik Kim, Thomas Joon Young Kim, Jeong-Wook Mun, Jin Woo Oh, In Gyu Choi & Sung-Hoon Ahn

  3. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Hae-Sung Yoon & Sangkee Min

  4. BK21 Plus Transformative Training Program for Creative Mechanical and Aerospace Engineers, Seoul National University, Seoul, 08826, Korea

    Dong-Hyeon Kim

  5. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Chung-Soo Kim

  6. William E. Boeing Department of Aeronautics & Astronautics, University of Washington, Seattle, WA, 98195, USA

    Jinkyu Yang

  7. Department of Railroad Integrated System, Woosong University, Daejeon, 34518, Korea

    Binayak Bhandari

  8. School of Mechanical Engineering, Changwon National University, Changwon, 51140, Korea

    Choon-Man Lee

  9. Boeing Research and Technology, Boeing, Seattle, Washington, 98108, USA

    Jeong-Beom Ihn

  10. Graduate School of Engineering Practice, Seoul National University, Seoul, 08826, Korea

    Sung-Hoon Ahn

  11. Institute of Engineering Research, Seoul National University, Seoul, 08826, Korea

    Sung-Hoon Ahn

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  1. Gil-Yong Lee
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  2. Mincheol Kim
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  18. Sung-Hoon Ahn
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Corresponding author

Correspondence to Sung-Hoon Ahn.

Additional information

Recommended by Editor Hyung Wook Park

Gil-Yong Lee received the B.S., M.S. and Ph.D. degrees from Seoul National University, Seoul, Korea in 2006, 2008, and 2013, respectively. After graduations, he conducted his research at the University of Washington, Seattle, WA from 2013 to 2016. Currently he is a Research Assistant Professor in the Institute of Advanced Machines and Design (IAMD) at the Seoul National University, Seoul, Korea. His research interests are in direct printing, nanoparticle printer, rapid prototyping, micro/nano fabrication, soft actuators and sensors, energy devices, composites, and acoustic metamaterials.

Sung-Hoon Ahn received the B.S. degree from University of Michigan, Ann Arbor, MI, USA in 1992, and M.S. and Ph.D. degrees from the Stanford University, CA, USA in 1994 and 1997, respectively. Currently, he is a Professor in the Dept. of Mechanical and Aerospace Engineering and Associate Dean in Graduate School of Engineering Practice at the Seoul National University, Seoul, Korea. His research interests include 3D Printing, Smart Soft Composite Materials, micro/ nano-scale fabrication (Laser, focused ion beam, milling drilling, and Nano Particle Deposition System), green manufacturing, energy device, and appropriate technology.

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Lee, GY., Kim, M., Quan, YJ. et al. Machine health management in smart factory: A review. J Mech Sci Technol 32, 987–1009 (2018). https://doi.org/10.1007/s12206-018-0201-1

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