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Development of features for blade rubbing defect classification in machine learning

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Abstract

This study has developed new features necessary for condition monitoring and diagnosis of rotating machinery. These features are developed using the phase change of vibration signal, which is characteristic of blade rubbing fault. These developed features are intended to identify the fault’s correct condition and severity of the rotating machinery. The difference between normal and blade rubbing fault was compared through experiments. The experimental model was produced to simulate a blade rubbing fault. The data were acquired through the experimental model and calculated using the developed features. Fault detection was confirmed by using genetic algorithm and machine learning that failure detection was possible using the developed features, it is expected that such study can evaluate the health of the rotating machinery.

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Acknowledgments

This research was supported by the grant entitled Development of Automatic Predictive Diagnosis Technology (Korea Hydro & Nuclear Power Central Research Institute, L18S065000). This work was supported by the Gyeongsang National University Fund for Professors on Sabbatical Leave, 2019.

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Authors and Affiliations

  1. Department of Energy and Mechanical Engineering, Gyeongsang National University, 2, Tongyeong-haean-ro, Tongyeong-si, 53064, Korea

    Dong Hee Park, Jeong Jun Lee, Deok Yeong Cheong, Ye Jun Eom & Byeong Keun Choi

  2. Korea Energy Technology Group, 17, Techno 4-ro, Yuseong-gu, Daejeon, Korea

    Seon Hwa Kim

Authors
  1. Dong Hee Park
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  2. Jeong Jun Lee
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  3. Deok Yeong Cheong
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  4. Ye Jun Eom
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  5. Seon Hwa Kim
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  6. Byeong Keun Choi
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Corresponding author

Correspondence to Byeong Keun Choi.

Additional information

Dong-Hee Park is certificated Doctor’s course degrees at the Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea. His areas of research are dynamic analysis of the shaft through FEM analysis and measurement for diagnosis.

Jeong-Jun Lee is certificated Doctor’s course degrees at the Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea. Areas of his research are dynamic analysis of the shaft through FEM analysis and measurement for diagnosis.

Deok-Yeong Cheong is certificated Doctor’s course degrees at the Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea. Areas of his research are dynamic analysis of the shaft through FEM analysis and measurement for diagnosis.

Ye-Jun Eom is certificated Master course degrees at the Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea. His areas of research are dynamic analysis of the shaft through FEM analysis and measurement for diagnosis.

Seon-hwa Kim is Chief Technical Officer at Korea Energy Technology Group Ltd. in South Korea. He received his Ph. D. degrees in Mechanical Engineering Science at the Graduate School of Engineering, Gyeongsang National University, Korea, in 2016. His specialty is autonomous robot eco-friendly power source technology and natural refrigerant cryogenic technology for hydrogen liquefaction.

Byeong-keun Choi is a Professor at the Department of Energy and Mechanical Engineering, Gyeongsang National University in Korea. He received his Ph.D. degree in Mechanical Engineering from Pukyong National University, Korea, in 1999. From 1999 to 2002, Dr. Choi worked at Arizona State University as an academic researcher. Dr. Choi’s research interests include vibration analysis and optimum design of rotating machinery, machine diagnosis, and prognosis and acoustic emission. He is listed on Who’s Who in the World, among others.

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Park, D.H., Lee, J.J., Cheong, D.Y. et al. Development of features for blade rubbing defect classification in machine learning. J Mech Sci Technol 38, 1–9 (2024). https://doi.org/10.1007/s12206-023-1201-3

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  • DOI: https://doi.org/10.1007/s12206-023-1201-3

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