Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
권호 표지
DOI QR코드

DOI QR Code

Real-time Monitoring System for Rotating Machinery with IoT-based Cloud Platform

회전기계류 상태 실시간 진단을 위한 IoT 기반 클라우드 플랫폼 개발

  • Jeong, Haedong (Dept. of System Design and Control, Ulsan Nat'l Institute Science and Technology) ;
  • Kim, Suhyun (Dept. of System Design and Control, Ulsan Nat'l Institute Science and Technology) ;
  • Woo, Sunhee (Dept. of System Design and Control, Ulsan Nat'l Institute Science and Technology) ;
  • Kim, Songhyun (Dept. of System Design and Control, Ulsan Nat'l Institute Science and Technology) ;
  • Lee, Seungchul (Dept. of System Design and Control, Ulsan Nat'l Institute Science and Technology)
  • 정해동 (울산과학기술원, 제어설계공학과) ;
  • 김수현 (울산과학기술원, 제어설계공학과) ;
  • 우선희 (울산과학기술원, 제어설계공학과) ;
  • 김송현 (울산과학기술원, 제어설계공학과) ;
  • 이승철 (울산과학기술원, 제어설계공학과)
  • Received : 2016.09.02
  • Accepted : 2017.03.08
  • Published : 2017.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

스마트 팩토리 시대가 열리면서 발전 플랜트에서 발생하는 빅데이터를 활용한 설비 유지 보수 방법론이 부각되고 있다. 본 연구에서는 데이터 기반 방법론의 효과적인 적용과 발전 플랜트 실시간 성능 모니터링을 위해 사물인터넷 기반 클라우드 플랫폼을 제안한다. Short-term Analysis에서는 사물인터넷 센서를 이용하여 학습된 건전성 인자와 패턴 비교를 통해 설비의 상태 진단과 결과 전송을 목적으로 한다. Long-term Analysis는 취합된 고차원 데이터를 활용하여 설비간 관계 파악과 인과관계 확인을 통한 트렌드 분석을 목적으로 한다. 분석 및 진단 결과는 클라우드 플랫폼의 웹 기반 시스템을 통해 시각화하여 사용자의 접근성을 향상시켜 장소나 접속 기기에 상관없이 데이터를 확인할 수 있도록 한다. 개발된 플랫폼의 성능 검증은 회전기계류 테스트베드로 진행한다.

Keywords

References

  1. Lee, S. H. and Yoon, B. D., 2015, "The Future of Industry 4.0 and Prognostics and Health Management," Journal of the Korean Society for Noise and Vibration Engineering, Vol. 25, No. 1, pp. 22-28.
  2. Kong, M. S., Chae, H. J. and Yu, B. H., 2016, "The Technology Tendency and Vision of IoT," Journal of the KSME, Vol. 56, No. 2, pp. 32-36.
  3. Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M., 2013, "Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions," Future Generation Computer Systems, Vol. 29, No. 7, pp. 1645-1660. https://doi.org/10.1016/j.future.2013.01.010
  4. Lee, D. S., 2015, "The Advent of Smart Factory and IoT Sensor," Process Control Instrumentation Technology, No. 6, pp. 81-85.
  5. Duda, R. O., Peter, E. H. and David, G. S., 2012, "Pattern Classification," John Wiley & Sons, USA, 2nd edition.
  6. Widodo, A. and Yang, B. S., 2007, "Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis," Mechanical Systems and Signal Processing, Vol. 21, No. 6, pp. 2560-2574. https://doi.org/10.1016/j.ymssp.2006.12.007
  7. Kankar, P. K., Sharma, S. C. and Harsha, S. P., 2011, "Fault Diagnosis of Ball Bearings using Machine Learning Methods," Expert Systems with Application, Vol. 38, No. 3, pp. 1879-1886.
  8. Duan, K., Keerthi, S. S., Chu, W., Shevade, S. K. and Poo, A. N. 2003. "Multi-category Classification by Softmax Combination of Binary Classifiers," In International Workshop on Multiple Classifier Systems, Springer Berlin Heidelberg. pp. 125-134.
  9. Jung, H. and Park, C. W., 2015, "Design and Implementation of MQTT Based Real-time HVAC Control Systems," Journal of the Korea Institute of Information and Communication Engineering, Vol. 19, No. 5, pp. 1163-1172. https://doi.org/10.6109/jkiice.2015.19.5.1163
  10. IBM Bluemix, http://www.ibm.com/cloudcomputing/bluemix/.
  11. Kim, D. E., Kim, S. W. and Kwon, S. K., 2015, "Real-Time Transmission System for Greenhouse Information Using MQTT and RTSP," Journal of Korea Multimedia Society, Vol. 18, No. 8, pp. 935-942. https://doi.org/10.9717/kmms.2015.18.8.935
  12. Lee, B. J., 1998, "Vibration and Maintenance Handbook," KEPCO, pp. 18-36.
  13. Lee, B. J., 1998, "Vibration and Maintenance Handbook," KEPCO, pp. 401-413.
  14. Cawley, G. C., Talbot, N. L. and Girolami, M., 2007, "Sparse Multinomial Logistic Regression via Bayesian $L_1$ Regularization," Advances in Neural Information Processing Systems, Vol. 19, pp. 209-216.