Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
권호 표지
DOI QR코드

DOI QR Code

Positioning-error Analysis of Vibration Sensors for Prognostics and Health Management in Rotating System

갠트리 크레인 호이스트의 건전성 평가를 위한 진동 모사시스템 구축과 데이터 통계 분석

  • Jang, Jaewon (Graduate School of Mokpo National Maritime University) ;
  • Han, Zhiqiang (Graduate School of Mokpo National Maritime University) ;
  • Zhang, Haiyang (Graduate School of Mokpo National Maritime University) ;
  • Oh, Daekyun (Department of Naval Architecture and Ocean Engineering, Mokpo National Maritime University)
  • 장재원 (목포해양대학교 대학원) ;
  • ;
  • ;
  • 오대균 (목포해양대학교 조선해양공학과)
  • Received : 2021.12.29
  • Accepted : 2022.04.27
  • Published : 2022.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, studies on the integrity of rotating machines, such as gantry cranes, which are used in the shipbuilding industry, have been actively conducted. Gantry cranes are driven at relatively low revolutions per minute (RPM), are frequently operated and stopped, and are impacted by external environmental factors, such as shock and noise in the measurement data. The purpose of this study was to construct a replica of a gantry crane hoist used in indoor shipbuilding and analyze the acquired data for errors caused by the shift in operating conditions (RPM) and the change in the position of the data acquisition sensor. Consequently, we observed that the error caused by differences in sensor positions did not occur significantly under low operating conditions but occurred significantly under relatively high operating conditions. Thus, we determined that both the operating condition and position of the acquisition sensor affected the data acquired by the rotary machine.

최근 회전 회전기계의 건전성 관련 연구가 활발하게 진행중이며, 조선업의 대표적인 회전기계인 갠트리 크레인에도 이를 적용하고자 하는 연구가 활발하게 진행되고 있다. 하지만 조선업의 갠트리 크레인의 경우 상대적으로 낮은 RPM으로 구동되고 잦은 운전과 정지가 이루어지며 충격, 소음 등의 외부환경 인자가 측정 데이터에 영향을 크게 미쳐 오차를 발생시킬 수 있다. 본 연구에서는 조선업의 내업공정에서 사용되는 갠트리 크레인의 Hoist 모사장비를 제작하여, 운전조건(RPM) 변화와 데이터 획득 센서의 위치 차이가 획득 데이터에 미치는 오차를 통계적으로 분석하였다. 연구결과 상대적으로 낮은 운전조건에서는 센서 위치 차이에 따른 획득 데이터의 오차는 크게 발생하지 않았으나, 상대적으로 높은 운전조건에서는 획득 데이터의 오차가 크게 발생하는 것으로 확인하였으며, 회전기계의 데이터 획득 시 운전조건과 획득 센서위치가 획득 데이터에 영향을 미치는 것으로 확인하였다.

Keywords

Acknowledgement

본 연구는 2021년도 정부(산업통상자원부)의 재원으로 한국산업기술 진흥원의 지원(20006978, IoT 및 AI 기반 블록 조립 공정용 디지털 트윈 기술개발)과 2022년도 정부(산업통상자원부)의 재원으로 한국산업기술 진흥원의 지원(P0017006, 2022년 산업혁신인재성장지원사업)을 받아 수행된 연구이며, 이에 감사드립니다.

References

  1. Choi, J. H.(2013), Introduction of Failure Prediction and Prognostics and Health Management Technology, The Korean Society of Mechanical Engineers, Vol. 53, No. 7, pp. 24-34.
  2. CMAK(2021), https://cmakusa.com/catalog/FLEXY-KITS.pdf.
  3. Duan, L., M. Yao, J. Wang, T. Bai, and L. Zhang(2016), Segmented Infraed Image Analysis for Rotating Machinery Fault Diagnosis, Infrared Physics and Technology, Vol. 77, pp. 267-276. https://doi.org/10.1016/j.infrared.2016.06.011
  4. Hwang, H. S.(2020), Study on Fault Detection Algorithms Based on Time-Domain Statistical Analysis for Rolling Bearing, Master Thesis, Dong-A University.
  5. Jeon, B. C., J. H. Jung, B. D. Youn, Y. W. Kim, and Y. C. Bae(2015), Evaluation of Datum Unit for Diagnostics of Journal-Bearing Systems, Transactions of the Korean Society of Mechanical Engineers, Vol. 39, No. 8, pp. 801-806. https://doi.org/10.3795/KSME-A.2015.39.8.801
  6. Ju, Y. J.(2020), Evaluation of Machine Learning Methods for Abnormality Detection and Diagnosis of Rotating Machine, Master Thesis, Inha University.
  7. Jung, U. and B. H. Koh(2015), Wavelet Energy-based Visualization and Classification of High-dimensional Signal for Bearing Fault Detection. Knowledge and Information Systems, Vol. 44, No. 1, pp. 197-215. https://doi.org/10.1007/s10115-014-0761-z
  8. Kowalski, C. T. and T. Orlowska-Kowalska(2003), Neural Networks Application for Induction Motor Faults Diagnosis, Mathematics and Computers in Simulation, Vol. 63, No. 3-5, pp. 435-448. https://doi.org/10.1016/S0378-4754(03)00087-9
  9. K Shipbuilding(2021), http://www.kshipbuilding.com/service/kor/yard_2020/yard.aspx.
  10. Lei, Y., J. Lin, Z. He, and M. J. Zuo(2013), A Review on Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery, Mechanical Systems and Signal Processing. Vol. 35, No. 1-2, pp. 108-126. https://doi.org/10.1016/j.ymssp.2012.09.015
  11. Li, B., C. Mo-Yuen, Y. Tipsuwan, and J. C. Hung(2000), Neural-Network-Based Motor Rolling Bearing Fault Diagnosis, IEEE Transactions on Industrial Electronics, Vol. 47, No. 5, pp. 1060-1069. https://doi.org/10.1109/41.873214
  12. Liu, R., B. Yang, E. Zio, and X. Chen(2018), Artificial Intelligence for fault Diagnosis of Rotating Machinery: A Review, Mechanical Systems and Signal Processing, Vol. 108, pp. 33-47. https://doi.org/10.1016/j.ymssp.2018.02.016
  13. NI(2021), NI 9234, https://www.ni.com/pdf/manuals/374238a_02.pdf.
  14. Ocak, H. and K. A. Loparo(2001), A New Bearing Fault Detection and Diagnosis Scheme Based on Hidden Markov Modeling of Vibration Signals, IEEE International Conference Proceedings on Acoustics, Speech and Signal Processing, Vol. 5, pp. 3141-3144.
  15. PCB(2021), PCB352C33, https://www.pcb.com/products?m=352C33.
  16. Shingala, M. C. and A. Rajyaguru(2015), Comparison of Post Hoc Tests for Unequal Variance, International Journal of New Technologies in Science and Engineering, Vol. 2, No. 5, pp. 22-33.
  17. Wilcox, R. R.(2003), Applying Contemporary Statistical Techniques, pp. 299-301.
  18. Yang, B. S. and K. J. Kim(2006), Application of Dempster-Shafer Theory in Fault Diagnosis of Induction Motors Using Vibration and Current Signal, Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 403-420. https://doi.org/10.1016/j.ymssp.2004.10.010
  19. Yu, Y., D. Yu, and C. Junsheng(2006), A Roller Bearing Fault Diagnosis Method Based on EMD Energy Entropy and ANN, Journal of Sound and Vibration, Vol. 294, No. 1-2, pp. 269-277. https://doi.org/10.1016/j.jsv.2005.11.002