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Data-driven health condition and RUL prognosis for liquid filtration systems

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Abstract

Because the clogging of filters leads to degradation and failure of mechanical systems, it is important to assess the health condition of a filter and predict its remaining useful life (RUL). Despite prior studies of liquid filtration systems, adequate attention has not been paid to data-driven health condition and RUL prognosis. Therefore, this study suggests a data-driven prognosis approach for liquid filtration systems. We define a health index (HI) for the filter under study and then predict HI values from the degradation point to the end-of-life using recurrent neural network algorithms, thereby yielding the filter’s RUL. As a result, the bidirectional LSTM achieved the best performance, and the RUL measured through HI prediction was close to the actual RUL. The proposed approach can be used for the maintenance of liquid filtration systems in various industries.

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Acknowledgments

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20204010600220). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1090228).

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Authors and Affiliations

  1. Department of Industrial Engineering, Konkuk University, Seoul, 05029, Korea

    Seunghyun Lee, Seungju Lee, Kwonneung Lee, Sangwon Lee, Jaemin Chung & Janghyeok Yoon

  2. School of Mechanical Engineering, Konkuk University, Seoul, 05029, Korea

    Chang-Wan Kim

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  1. Seunghyun Lee
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  2. Seungju Lee
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  7. Janghyeok Yoon
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Corresponding author

Correspondence to Janghyeok Yoon.

Additional information

Recommended by Editor Seungjae Min

Seunghyun Lee was born in Suwon, South Korea in 1996. He received the B.S. degrees in industrial engineering at Konkuk University, Seoul, South Korea, in 2021. He is currently working toward the M.S. degree in industrial engineering at Konkuk University, Seoul, South Korea. His research interests include data-driven prognostics and health management, social media mining for business opportunity discovery, and data mining for product/equipment monitoring.

Seungju Lee was born in Seoul, South Korea in 1996. His research interests include data-driven prognostics and health management and software development.

Kwonneung Lee was born in Seoul, South Korea in 1995. His research interests include data-driven prognostics and health management, statistical data analysis, and machine learning.

Sangwon Lee was born in Gumi, South Korea in 1996. His research interests include data-driven prognostics and health management and computer algorithms.

Jaemin Chung was born in Seoul, South Korea in 1995. He received the B.S. degrees in industrial engineering at Konkuk University, Seoul, South Korea, in 2020. He is currently working toward the M.S. degree in industrial engineering at Konkuk University, Seoul, South Korea. His research interests include patent/social media mining for business opportunity discovery and data-driven prognostics and health management.

Chang-Wan Kim was born in Pohang, South Korea in 1969. He received a B.S. degree in mechanical engineering, Hanyang University, Seoul, South Korea in 1987. He received an M.S. degree in mechanical engineering, Pohang University of Science and Technology, Pohang, South Korea in 1993. He received an M.S. degree in computational applied mathematics, and a Ph.D. in aerospace and engineering mechanics, University of Texas at Austin, Texas, USA, in 1997 and 1999, respectively. He is currently a Professor at the Department of Mechanical Engineering, Konkuk University, Seoul, South Korea. His research interests include vibration and noise analysis, multi-body dynamics, finite element analysis, and multi-physics system analysis.

Janghyeok Yoon was born in Daegu, South Korea in 1979. He received B.S. and M.S. degrees and a Ph.D. in industrial engineering at Pohang University of Science and Technology, Pohang, South Korea in 2002, 2004, and 2011, respectively. He has industrial experience at companies and research institutes such as LG and Korea Institute of Intellectual Property. He is currently an Associate Professor at the Department of Industrial Engineering, Konkuk University, Seoul, South Korea. His research interest is in technology intelligence-related topics, including technology forecasting, technology opportunity identification, technology road mapping, technology convergence, and business intelligence, including social media mining.

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Lee, S., Lee, S., Lee, K. et al. Data-driven health condition and RUL prognosis for liquid filtration systems. J Mech Sci Technol 35, 1597–1607 (2021). https://doi.org/10.1007/s12206-021-0323-8

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  • DOI: https://doi.org/10.1007/s12206-021-0323-8

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