한국가스학회지 (Journal of the Korean Institute of Gas)

  • 제28권2호
  • /
  • Pages.93-104
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  • 2024
  • /
  • 1226-8402(pISSN)
  • /
  • 2713-6922(eISSN)
한국가스학회 (The Korean Institute of GAS)
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ISO 20816 기반 회전기기 진동분석 자동화 알고리즘 개발

Development of Algorithm for Vibration Analysis Automation of Rotating Equipments Based on ISO 20816

  • 이재웅 (한국가스안전공사 가스안전연구원) ;
  • 이우귀연 (한국가스안전공사 가스안전연구원) ;
  • 오정석 (한국가스안전공사 가스안전연구원)
  • JaeWoong Lee (Institute of Gas Safety R&D, Korea Gas Safety Corporation) ;
  • Ugiyeon Lee (Institute of Gas Safety R&D, Korea Gas Safety Corporation) ;
  • Jeongseok Oh (Institute of Gas Safety R&D, Korea Gas Safety Corporation)
  • 투고 : 2024.05.16
  • 심사 : 2024.06.25
  • 발행 : 2024.06.30
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초록

산업현장에서 사용되는 회전기기의 원활한 작동 및 수명연장을 위해서는 설비진단이 필수적이다. 다양한 설비진단의 방법 중 진동진단은 다른 진단방법과 비교하여 불평형(unbalance), 축정렬 불량(misalignment), 베어링 결함(bearing fault), 기어 손상(worn gears), 소음(noise), 공진(resonance) 등 대부분의 초기 결함을 발견할 수 있다. 따라서, 진동분석은 산업현장에서 가장 범용적으로 사용되는 설비진단 방법이며, 설비의 상태를 기반으로 관리하는 예지보전(PdM) 기술로 유용하게 활용된다. 하지만, 진동진단 방법은 기준을 근거로 경험에 의존하여 수행되기 때문에 전문가에 의하여 진행된다. 따라서, 기존에 경험에 의존하여 수행하는 진동진단 방법을 지식화된 코드체계로 구축하여 누구나 쉽게 결함을 판단할 수 있는 시스템을 구축하여 설비의 신뢰성 구축에 기여하고자 한다. 진동측정에 대한 ISO-20816 기준을 근거로 알고리즘을 개발하였고, 석유화학공장 압축기, 수소충전소, 산업용 기계 등 다양한 실증현장에서 진동을 측정한 결과와 개발 시스템을 활용하여 분석한 결과를 비교하여 신뢰성을 검증하였다. 개발된 알고리즘을 통하여 산업현장에서 누구나 회전기기의 상태를 진단하고 결함을 조기에 파악하여 정확한 교체시점에 부품을 교체할 수 있는 예측유지보수(PdM)기술에 기여할 수 있다. 나아가, 정유산업시설, 운송, 생산 시설, 항공시설 등 다양한 산업현장에 적용 시 회전기기의 고장으로 인한 유지보수 비용과 다운타임(down time)의 절감에 이바지할수 있를 것으로 기대된다.

Facility diagnosis is essential for the smooth operation and life extension of rotating equipment used in industrial sites. Compared to other diagnostic methods, vibration diagnosis can find most of the initial defects, such as unbalance, alignment failure, bearing defects and resonance, compared to other diagnostic methods. Therefore, vibration analysis is the most commonly used facility diagnosis method in industrial sites, and is usefully used as a predictive preservation (PdM) technology to manage the condition of the facility. However, since the vibration diagnosis method is performed based on experience based on the standard, it is carried out by experts. Therefore, it is intended to contribute to the reliability of the facility by establishing a system that anyone can easily judge defects by establishing a vibration diagnosis method performed based on experience as a knowledgeable code system. An algorithm was developed based on the ISO-20816 standard for vibration measurement, and the reliability was verified by comparing the results of vibration measurement at various demonstration sites such as petrochemical plant compressors, hydrogen charging stations, and industrial machinery with the results of analysis using a development system. The developed algorithm can contribute to predictive maintenance (PdM) technology that anyone can diagnose the condition of the rotating machine at industrial sites and identify defects early to replace parts at the exact time of replacement. Furthermore, it is expected that it will contribute to reducing maintenance costs and downtime due to the failure of rotating machines when applied to various industrial sites such as oil refining facilities, transportation, production facilities, and aviation facilities.

키워드

과제정보

본 연구는 산업통상자원부 및 KETEP의 다중이용 에너지시설 안전진단 및 위험예측 안전기술개발사업(과제번호: 202159100200)의 연구결과로 수행되었습니다.

참고문헌

  1. Lee, K. S., Kim, J. W., "A study on strategy of condition based maintenance for rolling stock", 7th IET Conference on Railway Condition Monitoring, 1192, (2016) 
  2. Yan, R., Gao, R. X., "Complexity as a measure for machine health evaluation", IEEE Trans. Instrum. Meas, 53, 1327-1334, (2004) 
  3. Garcia, Marquez, F. P., Tobias, A. M., Pinar, Perez, J.M., "Condition monitoring of wind turbines: techniques and methods", Renew Energy, 46, 169-178, (2012) 
  4. Kumar, M., Shankar, Mukherjee, P., Mohan, Misra, N., "Advancement and current status of wear debris analysis for machine condition monitoring: a review", Ind. Lubr. Tribol., 65, 3-11, (2013) 
  5. Khan, M. A., Starr, A. G., "Wear debris: basic features and machine health diagnostics", Insight Non-Destr. Test Cond. Monit., .48, 470-476, (2006) 
  6. Tribol. Lubr., "Review of Machine Condition Monitoring with Oil Sensors-Types of Sensors and Their Functions", Tribology and Lubricants, 36, 297~306, (2020) 
  7. Yang, Hongyu and Mathew, Joseph and Ma, Lin, "Vibration feature extraction techniques for fault diagnosis of rotating machinery : a literature survey," In: Asia-Pacific Vibration Conference, (2003) 
  8. Kim Y W, et al., "Analysis and processing of shaft angular velocity signals in rotating machinery for diagnostic applications. in Acoustics, Speech, and Signal Processing," ICASSP-95, 5, 2971-2974, (1995) 
  9. McFadden P D and Smith J D, "The vibration produced by multiple point defects in a rolling element bearing." Journal of Sound and Vibration, 98(2): 263-273, (1985) 
  10. Tandon N and Choudhury A, "A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings," Tribology International, 32(8): 469-480, (1999) 
  11. Lebold M, et al., "Review of vibration analysis methods for gearbox diagnostics and prognostics," 54th meeting of the society for machinery failure prevention technology, Virginia Beach, VA, 623-634, (2000) 
  12. H.A. Mubarak, F.A. Khan, M.M. Oskay, "ESP Failures Analysis Solutions in Divided Zone Case Study.", SPE 81488 Presented at the SPE 13th Middle East Oil Show and Conference, (2003) 
  13. G. Regres, M. Fontana, M. Ribeiro, "Electric submersible pump vibration analysis under several operational conditions for vibration fault differential diagnosis", Ocean. Eng. 219, (2021) 
  14. Tandon N and Choudhury A, "A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings," Tribology International, 32(8): 469-480, (1999) 
  15. V.Manish, V.Harshlatac, P.Rajput, "Vibration Analysis & Condition Monitoring for Rotating Machines:A Review", 5th International Conference of Materials Processing and Characterization, 4(2), 2659-2664, (2017) 
  16. Yang D-M, et al., "Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks", Mechanical Systems and Signal Processing, 16(2-3): 391-411, (2002) 
  17. Thuy Chu a, Tan Nguyen a, Hyunsang Yoo a, " A review of vibration analysis and its applications" Heliyon, 10(5), e26282, (2024) 
  18. Gh. Buzdugan, E. Mihailescu, M. Rades, Vibration Measurement, Springer Netherlands, (2013) 
  19. International Organization for Standardization, Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Non-rotating Parts - Part 1: General Guidelines, ISO Standard No. 10816-1:1995, (1995) 
  20. Yang D-M, et al.," Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks" Mechanical Systems and Signal Processing, 16(2-3): 391-411, (2002) 
  21. Lee J G, Ryu H G, "Performance Comparison of OFDM Based on Fourier Transform and Wavelet OFDM Based on Wavelet Transform", Journal of Hydrogen and New Energy, 34(6), 682~688, (2023) 
  22. R.B. Randall, J. Antoni, "Rolling element bearing diagnostics A tutorial", Mechanical Systems and Signal Processing, 25, 485520, (2011) 
  23. Pornchai N, Dutsadee J., "Bearing Fault Monitoring by Comparison with Main Bearing Frequency Components Using Vibration Signal", Business and Industrial Research (ICBIR), 292-296, (2018) 
  24. Kim J Y, Choi G T, "A Study on Failure Mode and Effect Analysis of Hydrogen Fueling Nozzle Used in Hydrogen Station", Journal of Hydrogen and New Energy, 34(6), 682~688, (2023) 
  25. Hitesh H., Vikas J., I.V. Y, "The Common Faults and Analysis of Hydrogen Compressor",IOP Conference Series: Earth and Environmental Science, (2020)