Journal of the Computational Structural Engineering Institute of Korea (한국전산구조공학회논문집)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
권호 표지
DOI QR코드

DOI QR Code

Feature Extraction for Bearing Prognostics using Weighted Correlation Coefficient

상관계수 가중치를 이용한 베어링 수명예측 특징신호 추출

  • Kim, Seokgoo (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Lime, Chaeyoung (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Choi, Joo-Ho (School of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
  • 김석구 (한국항공대학교 항공우주 및 기계공학과) ;
  • 임채영 (한국항공대학교 항공우주 및 기계공학과) ;
  • 최주호 (한국항공대학교 항공우주 및 기계공학부)
  • Received : 2018.01.03
  • Accepted : 2018.01.10
  • Published : 2018.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Bearing is an essential component in many rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management(PHM), in which the key issue is to establish a proper feature reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics because it tends to increase abruptly at the end of life. This paper proposes a new feature extraction method using the envelope analysis and weighted sum with correlation coefficient. The developed method is demonstrated using the IMS bearing data given by NASA Ames Prognostics Data Repository. Results by the proposed feature are compared with those by conventional approach.

베어링은 많은 회전체에서 사용되는 핵심부품으로, 예기치 않은 고장을 방지하기 위해 많은 연구가 집중되고 있다. 이때 중요한 것은 되도록 초기에 건전성 상태를 잘 나타내는 적절한 특징신호를 추출하는 것이다. 그러나 기존의 연구들은 주로 진단관점에서 특징신호를 추출하여 고장예지에는 적합하지 않은 측면이 있었다. 본 논문에서는 이러한 문제를 극복하기 위해 베어링 고장 주파수의 에너지와 시간 사이의 상관계수 가중 합을 이용하여 베어링 수명 예측에 용이한 특징신호를 추출하는 방법을 개발하였다. 그 결과 일반적으로 고장진단에서 많이 사용되고 있는 특징신호인 RMS에 비해서 결함 초기부터 단조로운 증가 경향의 특징신호를 추출함을 알 수 있었다. 이를 입증하기 위해서 NASA Ames에서 제공한 IMS bearing 진동 데이터를 이용하였고 제시한 특징신호와 일반적인 RMS와 의 거동을 비교하여 유효성을 검증하였다.

Keywords

References

  1. An, D., Kim, N.H., Choi, J.H. (2012) A Comparison Study of Model Parameter Estimation Methods for Prognostics, J. Comput. Struct. Eng. Inst. Korea, 25(4), pp.355-362. https://doi.org/10.7734/COSEIK.2012.25.4.355
  2. Devore, J.L. (2011) Probability and Statistics for Engineering and the Sciences. Cengage Learning, p.769.
  3. Jardine, A.K., Lin, D., Banjevic, D. (2006) A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance, Mech. Syst. & Signal Proc., 20(7), pp.1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
  4. Lee, J., Qiu, H., Yu, G., Lin, J. (2007) Rexnord Technical Services, 'Bearing Data Set', IMS, University of Cincinnati, NASA Ames Prognostics Data Repository.
  5. Li, H., Zheng, H. (2008) Bearing Fault Detection using Envelope Spectrum Based on EMD and TKEO, In Fuzzy Systems and Knowledge Discovery, FSKD'08. Fifth Int. Conf. on, 3, pp.142-146.
  6. Park, S., Kim, J.W., Lee, C., Lee, J., Gil, H.B. (2012) Local Fault Detection Technique for Steel Cable using Multi-Channel Magnetic Flux Leakage Sensor, J. Comput. Struct. Eng. Inst. Korea, 25(4), pp.287-292. https://doi.org/10.7734/COSEIK.2012.25.4.287
  7. Park, S., Choi, J.H. (2017) A Study on Fault Classification by EEMD Application of Gear Transmission Error, J. Comput. Struct. Eng. Inst. Korea, 30(2), pp.169-177. https://doi.org/10.7734/COSEIK.2017.30.2.169
  8. Qiu, H., Lee, J., Lin, J., Yu, G. (2003) Robust Performance Degradation Assessment Methods for Enhanced Rolling Element Bearing Prognostics, Adv. Eng. Inform., 17(3), pp.127-140. https://doi.org/10.1016/j.aei.2004.08.001
  9. Randall, R.B., Antoni, J. (2011) Rolling Element Bearing Diagnostics-A Tutorial. Mech. Syst. & Signal Proc., 25(2), pp.485-520. https://doi.org/10.1016/j.ymssp.2010.07.017
  10. Siegel, D., Ly, C., Lee, J. (2012) Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure. IEEE Trans. Reliab., 61(4), pp.846-857. https://doi.org/10.1109/TR.2012.2220697
  11. Tabrizi, A., Garibaldi, L., Fasana, A., Marchesiello, S. (2015) A Novel Feature Extraction for Anomaly Detection of Roller Bearings Based on Performance Improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator. Int. J. Progn Health Manage., 6(026), p.10.
  12. Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., Tripot, G. (2011) Estimation of the Remaining Useful Life by using Wavelet Packet Decomposition and HMMs. In Aerospace Conf., 2011 IEEE, pp.1-10.
  13. Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M.E., Vachtsevanos, G. (2011) A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection. IEEE Trans. Indus. Elect., 58(5).
  14. Qiu, H., Lee, J., Lin, J., Yu, G. (2006) Wavelet Filter-Based Weak Signature Detection Method and its Application on Rolling Element Bearing Prognostics, J. Sound & Vib., 289(4), pp.1066-1090. https://doi.org/10.1016/j.jsv.2005.03.007