한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제25권11호
  • /
  • Pages.1547-1556
  • /
  • 2022
  • /
  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
DOI QR코드

DOI QR Code

시분할 CNN-LSTM 기반의 시계열 진동 데이터를 이용한 회전체 기계 설비의 이상 진단

Anomaly Diagnosis of Rotational Machinery Using Time-Series Vibration Data Based on Time-Distributed CNN-LSTM

  • Kim, Min-Ki (Dept. of Computer Science, Gyeongsang National University, Engineering Research Institute)
  • 투고 : 2022.09.05
  • 심사 : 2022.10.14
  • 발행 : 2022.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

키워드

참고문헌

  1. K. Choi. J. Yi, C. Park, and S. Yoon, "Deep Learning for Anomaly Detection in TimeSeries Data: Review, Analysis, and Guidelines," IEEE Access, Vol. 9, pp. 120043-120065, 2021. https://doi.org/10.1109/ACCESS.2021.3107975
  2. B. Samanta and K.R. Al-Balushi, "Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features," Mechanical Systems and Signal P rocessing, Vol. 17, No. 2, pp. 317-328, 2003. https://doi.org/10.1006/mssp.2001.1462
  3. G. Hong and D. Suh, "Supervised-LearningBased Intelligent Falut Diagnosis for Mechanical Equipment," IEEE Access, Vol. 9, pp. 116147-116162, 2021. https://doi.org/10.1109/ACCESS.2021.3104189
  4. K. Lee, C. Vununu, K. Moon, S. Lee, and K. Kwon, "Automatic Machine Fault Diagnosis System Using Discrete Wavelet Transform and Machine Learning," J ournal of Korea Multimedia Society, Vol. 20, No. 8, pp. 1299-1311, 2017.
  5. G. Lee, M. Jung, and M. Song, "Unsupervised Anomaly Detection of the Gas Turbine Operation via Convolutional Auto-Encoder," Proceedings of the IEEE International Conference on Prognostics and Health Management, pp. 1-6, 2020.
  6. C. Zhang, J. Liu, W. Chen, J. Shi, M. Yao, X. Yan, N. Xu, and D. Chen, "Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering," Security and Communication Networks, Vol. 2021, pp. 1-8, 2021.
  7. Z. Chen, C.K. Yeo, B.S. Lee, and C.T. Lau, "Autoencoder-Based Network Anomaly Detection,'' Proceedings of the Wireless Telecommunications Symposium, pp. 1-5, 2018.
  8. R. Chalapathy and S. Chawla, "Deep Learning for Anomaly Detection: A Survey," arXiv Preprint, arXiv:1901.03407, pp. 1-50, 2019
  9. D.H. Pandya, S.H. Upadhyay, and S.P. Harsha, "Fault Diagnosis of Bearing with Supervised Machine Learning Techniques," Proceedings of the International Conference on Innovations in Design and Manufacturing," pp. 1-5, 2012.
  10. S.R. Kumar, D.B. Phavithraa, and R.G. Gayathri, "Supervised Machine Learning Based Anomaly Detection and Diagnosis in Grid Connected Photovoltaic Systems," Proceedings of the International Conference on Combinatorial and Optimization, pp. 1-15, 2021.
  11. Y. He and J. Zhao, "Temporal Convolutional Networks for Anomaly Detection in Time Series," Proceedings of the IOP Conference Series: Journal of Physics, Vol. 1213, pp. 1-6, 2019.
  12. L. Shen, Z. Li, and J.T. Kwok, "Timeseries Anomaly Detection Using Temporal Hierarchical One-Class Network," Proceedings of the Neural Information Processing Systems, pp. 1-11, 2020.
  13. P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, "Long Short Term Memory Networks for Anomaly Detection in Time Series," Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 89-94, 2015.
  14. L. Yu, J. Qu, F. Gao, and Y. Tian, "A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM," Shock and Vibration, Vol. 2019, pp. 1-10, 2019. https://doi.org/10.1155/2019/8959845
  15. C.-J. Huang and P.-H. Kuo, "A Deep CNNLSTM Model for Particulate Matter(PM2.5) Forecasting in Smart Cities," Sensors, Vol. 18, pp. 1-22, 2020. https://doi.org/10.3390/s18010001
  16. Guidelines for AI data construction and utilization: vibration/current data of machinery, https://aihub.or.kr/aihubdata/data/view.do?curr Menu=115&topMenu=100&aihubDataSe=realm&dataSetSn=238 (accessed November 1, 2022).
  17. V. Hariharan and P. Srinivasan, "New Approach of Classification of Rolling Element Bearing Fault Using Artificial Neural Network," Journal of Mechanical Engineering, Vol. 40, No. 2, pp. 119-130, 2009. https://doi.org/10.3329/jme.v40i2.5353
  18. J. Cho and L. Lee, "Cleaning Noises from Time Series Data with Memory Effects," Journal of The Korea Society of Computer and Information, Vol. 25, No. 4, pp. 37-45, 2020. https://doi.org/10.9708/JKSCI.2020.25.04.037
  19. I. Botunac, A. Panjkota, and M. Matetic, "The Importance of Time Series Data Filtering for Predicting the Direction of Stock Market Movement Using Neural Networks," Proceedings of the International Symposium on Intelligent Manufacturing and Automation, Vol. 30, pp. 886-891, 2019.
  20. J. Donahue, L.A. Hendricks, M. Rohrbach, S. Venugopalan, S. Guadarrama, K. Saenko, and T. Darrell, "Long-Term Recurrent Convolutional Networks for Visual Recognition and Description," arXiv Preprint, arXiv:1411.4389, pp. 1-14, 2015.
  21. Case Western Reserve University Bearing Data Center, Seeded Fault Test Data, https://engineering.case.edu/bearingdatacenter (accessed November 1, 2022).
  22. J. Seo, J. Park, J. Yoo, and H. Park, "Anomaly Detection System in Mechanical Facility Equipment: Using Long Short-Term Memory Variational Autoencoder," Journal of Korean Society for Quality Management, Vol. 49, No. 4, pp. 581-594, 2021. https://doi.org/10.7469/JKSQM.2021.49.4.581
  23. J. Seo, A Study on the Anomaly Detection using Machine Facility Data: Focused on Supervised Machine Learning, Yonsei University Master's thesis, 2021.