한국기계가공학회지 (Journal of the Korean Society of Manufacturing Process Engineers)

  • 제20권9호
  • /
  • Pages.35-41
  • /
  • 2021
  • /
  • 1598-6721(pISSN)
  • /
  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
권호 표지
DOI QR코드

DOI QR Code

SVM 기법을 적용한 구름베어링의 부식 고장진단

Corrosion Failure Diagnosis of Rolling Bearing with SVM

  • 고정일 (금오공과대학교 기계시스템공학과) ;
  • 이의영 (금오공과대학교 기계시스템공학과) ;
  • 이민재 (금오공과대학교 기계시스템공학과) ;
  • 최성대 (금오공과대학교 기계시스템공학과) ;
  • 허장욱 (금오공과대학교 기계시스템공학과)
  • Go, Jeong-Il (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Lee, Eui-Young (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Lee, Min-Jae (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Choi, Seong-Dae (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Hur, Jang-Wook (Department of Mechanical System Engineering, Kumoh National institute of Technology)
  • 투고 : 2021.05.27
  • 심사 : 2021.07.19
  • 발행 : 2021.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

키워드

과제정보

이 논문은 2019년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. 2019R1I1A3A01063935).

참고문헌

  1. Choi, J. H., "Introduction of Failure Prognosis and PHM", Journal of The Korean Society of Mechanical Engineers, Vol. 53, No. 7, pp. 24-34, 2013.
  2. Jeon, H. K., Kim, J. S., Kim, B. J. and Kim, W. J., "A Study on the fault diagnosis of ratating machine by machine learning", Journal of The Acoustical Society of Korea, Vol. 39, pp. 263-269, 2020. https://doi.org/10.7776/ASK.2020.39.4.263
  3. Hong, D. P. and Kim, W. T., "Quantitative NDE Thermography for Fault Diganosis of Ball Bearings with Micro-Foreign Substances", Journal of The Korean Society for Nondestructive Testing, Vol. 34, pp. 305-310, 2014. https://doi.org/10.7779/JKSNT.2014.34.4.305
  4. Muzakkir, S. M., Lijesh, K. P. and Hirani, H., "Failure Mode and Effect Analysis of Journal Bearing", International Journal of Applied Engineering Research, Vol. 10, No. 16, pp. 36843-36850, 2015.
  5. Ahn, D. G., Yoo, J. M. and Jang, J. S., "How to Perform FMEA Effectively for Weapon System Development Stage", Journal of Applied Reliability, Vol. 21, No. 1, pp. 45-60, 2021. https://doi.org/10.33162/JAR.2021.3.21.1.45
  6. Fitch, J., "How Water Causes Bearing Failure", Machinery Lubrication, Vol. 7, pp. 2-4, 2008.
  7. Alam, S. T. and Hu,r J. W., "EEMD assisted supervised learning for the fault diagnosis of BLDC motor using vibration signal", Journal of Mechanical Science and Technology, Vol. 34, No. 10, pp. 3981-3990, 2020. https://doi.org/10.1007/s12206-020-2208-7
  8. Kwak, T. H., Song, A. R. and Kim, Y. G., "The Impact of the PCA Dimensionality Reduction for CNN based Hyperspectral Image Classification", Korean Journal of Remote Sensing, Vol. 35, No. 6-1, pp. 959-971, 2019.
  9. Jung, S. H., Lee, G. I., Kim, J. M., Hong, S. W., Chung, Y. S. and Jung, H. K., "A Comparison of Target Identification using Support Vector Machine with Various Kernel Functions" The Korean Institute of Electrical Engineers, Vol. 12, No. 1, pp. 77-95, 2020.
  10. Yoo, Y. S., Kim, D. H., Kim, S. and Hur, J. W., "Fault Prognostics of a SMPS based on PCA-SVM" Journal of The Korean Society of Manufacturing Process Engineers, Vol. 19, No. 9, pp. 47-52, 2020.
  11. Lee, G. H., Shin, B. C. and Hur, J. W., "Fault Classification of Gear Pumps Using SVM", Journal of Applied Reliability, Vol. 20, No. 2, pp. 189-196, 2020.
  12. Hong, C. H., "Confusion Plot for the Confusion Matrix", Journal of the Korean Data And Information Science Society, Vol. 32, No. 2, pp. 1065-1066, 2018.