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A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems

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Abstract

Prognostics and health management (PHM) has emerged as an intelligent solution to improve the availability of manufacturing systems. PHM consists of system health monitoring, feature extraction, fault diagnosis, and fault prognosis through remaining useful life estimation. However, the application of PHM to manufacturing systems is challenging because systems have become more complex and uncertain. In particular, small and medium-sized enterprises have difficulty in applying PHM due to the lack of internal expertise, time and resources for research and development. The objective of this paper is to develop a framework to provide a readily usable and accessible guideline for PHM application to manufacturing systems. A survey was performed to gather the current practices in dealing with system failures and maintenance strategies in the field. A framework was developed for giving a guideline for PHM application based on common core modules across manufacturing systems and their kinds with respect to the amount of available data and domain knowledge. A reference table was developed to track the PHM techniques for feature extraction, fault diagnosis, and fault prognosis. Finally, fault prognosis of a system was conducted as a case study, following the framework and the reference table to verify its practical use.

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  1. Insun Shin, Junmin Lee, Jun Young Lee, and Kyusung Jung contributed equally to this work as the first author

Authors and Affiliations

  1. School of Mechanical, Aerospace, and Nuclear Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulju-gun, Ulsan, 44919, Republic of Korea

    Insun Shin & Daeil Kwon

  2. Department of Mechanical and Aerospace Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Junmin Lee & Byeng D. Youn

  3. School of Automotive Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea

    Jun Young Lee & Hyun Soo Jang

  4. School of Aerospace and Mechanical Engineering, Korea Aerospace University, 76, Hanggongdaehak-ro, Deogyang-gu, Goyang-si, Gyeonggi-do, 10540, Republic of Korea

    Kyusung Jung & Joo-Ho Choi

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  1. Insun Shin
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Shin, I., Lee, J., Lee, J.Y. et al. A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems. Int. J. of Precis. Eng. and Manuf.-Green Tech. 5, 535–554 (2018). https://doi.org/10.1007/s40684-018-0055-0

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  • DOI: https://doi.org/10.1007/s40684-018-0055-0

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