한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제12권8호
  • /
  • Pages.31-37
  • /
  • 2021
  • /
  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
권호 표지
DOI QR코드

DOI QR Code

냉동시스템 고장 진단 및 고장유형 분석을 위한 3단계 분류 알고리즘에 관한 연구

A study on the 3-step classification algorithm for the diagnosis and classification of refrigeration system failures and their types

  • 이강배 (동아대학 경영정보학과) ;
  • 박성호 (동아대학 경영정보학과) ;
  • 이희원 (동아대학 경영정보학과) ;
  • 이승재 (동아대학 경영정보학과) ;
  • 이승현 (동아대학 경영정보학과)
  • 투고 : 2021.05.06
  • 심사 : 2021.08.20
  • 발행 : 2021.08.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

산업의 발전으로 도시화로 인해 건물의 규모가 커지면서, 건물의 공기 정화 및 쾌적한 실내 환경을 유지의 필요성 또한 증가하고 있다. 냉동 시스템의 모니터링 기술의 발전으로 건물 내에 발생하는 전력 소모량을 관리할 수 있게 되었다. 특히 상업용 건물에서 발생하는 전력 소모량 중 약 40%가 냉동 시스템에서 일어난다. 따라서 본 연구 냉동시스템 고장진단 알고리즘을 개발하기 위해서 냉동시스템의 구조를 이해하고, 냉동 시스템의 운영과정에서 발생하는 데이터를 수집 분석하여 다양한 유형과 심각도를 가지는 고장 상황을 조기에 신속하게 탐지 분류하고자 하였다. 특히 분류가 어려운 고장 유형들의 분류 정확도를 향상시키기 위하여 3단계 진단 및 분류 알고리즘을 개발하여 제안하였다. 다수의 실험과 초모수 (hyper parameter) 최적화 과정을 거쳐 각 단계에 적합한 분류 모형으로 SVM과 LGBM에 기반 한 모형을 제시하였다. 본 연구에서는 고장에 영향을 미치는 특성을 최대한 보존하면서, 선행연구에서 어려움을 겪었던 냉매 관련 고장을 포함한 모든 고장 유형을 우수한 결과로 도출하였다.

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

키워드

과제정보

This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-02091,Development and Commercialization of IoT-based refrigerated container real-time monitoring and BigData / AI-based failure predictive service platform to strengthen competitiveness of shipping & logistics company) and Korea Evaluation Institute of Industrial Technology(KEIT) grant funded by the Korea government(MOTIE) (No.2020-0-02091,Development and Commercialization of IoT-based refrigerated container real-time monitoring and BigData / AI-based failure predictive service platform to strengthen competitiveness of shipping & logistics company).

참고문헌

  1. B. Jin, D. Li, S. Srinivasan & S. K. Ng. (2019). Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks. 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), 2019. DOI : 10.1109/ICPHM.2019.8819438
  2. Y. Xia, Q. Ding, Z. Li & A. Jiang. (2021). Fault detection for centrifugal chillers using a Kernel Entropy Component Analysis (KECA) method. Journal of Building Simulation, 14(1), 53-61 DOI : 10.1007/s12273-019-0598-1
  3. Q. Jiehui, Z. Hanyuan, Z. Guiqing & H. Hao. (2021). Incipient fault detection of chiller based on improved CVA, 5th International Workshop on Advances in Energy Science and Environment Engineering (AESEE 2021), 257(1), 1-5. DOI : 10.1051/e3sconf/202125701062
  4. G. N. Li, Y. P. Hu, Q. J. Mao, C. H. Zhou & L. Z. Jiao. (2019). A deep neural network based fault diagnosis method for centrifugal chillers. 2019 IOP Conference Series: Earth and Environmental Science, (pp. 1-6), Poland DOI : 10.1088/1755-1315/238/1/012047
  5. Y. S. Moon, S. J. Park, J. W. Jung, H. R. Cho & J. J. Kim. (2017). Temperature Control Algorithm for Reefer Container. Journal of the Korea Institute of Information and Communication Engineering, 21(12), 2080-2386. DOI : 10.6109/jkiice.2017.21.12.2380
  6. Z. Wang, Y. Dong, W. Liu & Z. Ma. (2020). A Novel Fault Diagnosis Approach for Chillers Based on 1-D Convolutional Neural Network and Gated Recurrent Unit. Journal of Sensors, 20(9), 1-20. DOI : 10.3390/s20092458
  7. K. Lee, S. Park, S, Sung & D. Park. (2019). A Study on the Prediction of CNC Tool Wear Using Machine Learning Technique. Journal of the Korea Convergence Society, 10(10), 15-21. DOI : 10.15207/JKCS.2019.10.11.015
  8. A. Beghi, L. Cecchinato, C. Corazzol, M. Rampazzo, F. Simmini & G. A. Susto. (2014). A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems. IFAC Proceedings Volumes, 47(3), 1953-1958. DOI : 10.3182/20140824-6-ZA-1003.02382
  9. G. Li, Y. Huab, H. Chen, L. Shen, H. Li, M. Hu, J. Liu & Kaizheng Sun. (2016). An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm. Journal of Energy and Buildings, 116(15), 104-113. DOI : 10.1016/j.enbuild.2015.12.045
  10. Y. Wang, Z. Wang, S. He & Z. Wang. (2019). A practical chiller fault diagnosis method based on discrete Bayesian networkUne methode pratique de diagnostic des anomalies du refroidisseur basee sur un reseau bayesien discret. Journal of International Journal of Refrigeration, 102, 159-167. DOI : 10.1016/j.ijrefrig.2019.03.008
  11. H. Han, X. Cui, Y. Fan & H. Qing. (2019). Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features. Journal of Applied Thermal Engineering, 154, 540-547 DOI : 10.1016/j.applthermaleng.2019.03.111
  12. P. K. Kankar, S. C. Sharma & S. P. Harsha. (2019). Fault diagnosis of ball bearings using machine learning methods. Joural of Expert Systems with Applications, 38, 1876-1886. DOI : 10.1016/j.eswa.2010.07.119
  13. Z. Wang, Y. Dong, W. Liu & Z. Ma. (2020). A Novel Fault Diagnosis Approach for Chillers Based on 1-D Convolutional Neural Network and Gated Recurrent Unit. Joural of Sensors, 20(9), 1-20. DOI : 10.3390/s20092458
  14. X. Liu, Y. Li, X. Liu & J. Shen. (2018). Fault diagnosis of chillers using very deep convolutional network. Chinese Automation Congress, (pp 1274- 1279), China. DOI : 10.1109/CAC.2018.8623749
  15. J. Liu, D. Shi, G. Li, Y. Xie, K. Li, B. Liu & Z. Ru. (2020). Data-driven and association rule mining-vased fault diagnosis and action mechanism analysis for building chillers. Joural of Energy and Buildings, 216, 1-16. DOI : 10.1016/j.enbuild.2020.109957