Journal of Navigation and Port Research (한국항해항만학회지)

Korean Institute of Navigation and Port Research (한국항해항만학회)
권호 표지
DOI QR코드

DOI QR Code

Comparison of Prediction Accuracy Between Classification and Convolution Algorithm in Fault Diagnosis of Rotatory Machines at Varying Speed

회전수가 변하는 기기의 고장진단에 있어서 특성 기반 분류와 합성곱 기반 알고리즘의 예측 정확도 비교

  • Received : 2022.06.10
  • Accepted : 2022.06.29
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

본 연구는 정상 가동 중에도 회전수가 변하는 기기의 이상 및 고장 진단 방안을 다루고 있다. 회전수가 변함에 따라 비정상적 시계열 특성을 내포한 센서 데이터에 기계학습을 적용할 수 있는 절차를 제시하고자 하였다. 기계학습으로는 k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Random Forest을 사용하여 이상 및 고장 진단을 수행하였다. 또한 진단 정확성을 비교할 목적으로 이상 감지에 오토인코더, 고장진단에는 합성곱 기반의 Conv1D도 추가로 이용하였다. 비정상적 시계열로부터 통계 및 주파수 속성으로 구성된 시계열 특징 벡터를 추출하고, 추출된 특징 벡터에 정규화 및 차원 축소 기법을 적용하였다. 특징 벡터의 선택과 정규화, 차원 축소 여부에 따라 달라지는 기계학습의 진단 정확도를 비교하였다. 또한, 적용된 학습 알고리즘 별로 초매개변수 최적화 과정과 적층 구조를 설명하였다. 최종적으로 기존의 심층학습과 비교하여, 기계학습도 가변 회전기기의 고장을 정확하게 진단할 수 있는 절차를 제시하였다.

Keywords

Acknowledgement

본 연구는 2022년도 산업통산자원부(해양수산부) 및 산업기술평가관리원(해양수산과학기술진흥원) 연구비 지원으로 수행된 '자율운항선박 기술개발사업 (20011164, 자율운항선박 핵심 기관시스템 성능 모니터링 및 고장예측/진단 시스템 기술 개발연구)'의 연구결과입니다.

References

  1. Bartelmus, W., Chaari, F., Zimroz, R. and Haddar, M.(2010), "Modelling of gearbox dynamics under time-varying nonstationary load for distributed fault detection and diagnosis", European Journal of Mechanics-A/Solids, Vol. 29, No. 4, pp. 637-646. https://doi.org/10.1016/j.euromechsol.2010.03.002
  2. Breiman, L.(2001), "Random forests", Machine Learning, Vol. 45, No. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  3. Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., and Dick, O. B.(2012), "Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis".
  4. Chawla, N. V., Bowyer, K. W., Hall, L. O. and Kegelmeyer, W. P.(2002), "SMOTE: synthetic minority over-sampling technique", Journal of Artificial Intelligence Research, Vol. 16, pp. 321-357. https://doi.org/10.1613/jair.953
  5. Cover, T. and Hart, P.(1967), "Nearest Neighbor Pattern Classification", IEEE Transactions on Information Theory, Vol. 13, No. 1, pp. 21-27. https://doi.org/10.1109/TIT.1967.1053964
  6. de Lima, A. A. et al.(2013), "On fault classification in rotating machines using fourier domain features and neural networks", In 2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS), IEEE, pp. 1-4.
  7. Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S. and Acharya, U. R.(2018), "Deep learning for healthcare applications based on physiological signals: A review", Computer methods and programs in biomedicine, Vol. 161, pp. 1-13. https://doi.org/10.1016/j.cmpb.2018.04.005
  8. Feng, Z. and Liang, M.(2014), "Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis", Renewable Energy, Vol. 66, pp. 468-477. https://doi.org/10.1016/j.renene.2013.12.047
  9. Kan, M. S., Tan, A. C. and Mathew, J.(2015), "A review on prognostic techniques for non-stationary and non-linear rotating systems", Mechanical Systems and Signal Processing, Vol. 62, pp. 1-20. https://doi.org/10.1016/j.ymssp.2015.02.016
  10. Lee, J. H.(2021), "Experimental Study on Application of an Anomaly Detection Algorithm in Electric Current Datasets Generated from Marine Air Compressor with Time-series Features", Journal of the Korean Society of Marine Environment & Safety, Vol. 27, No. 1, pp. 127-134. https://doi.org/10.7837/kosomes.2021.27.1.127
  11. Li, W., Zhu, Z., Jiang, F., Zhou, G., and Chen, G.(2015), "Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method", Mechanical Systems and Signal Processing, Vol. 50, pp. 414-426. https://doi.org/10.1016/j.ymssp.2014.05.034
  12. Liao, Y., Huang, R., Li, J., Chen, Z. and Li, W.(2020), "Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed", IEEE Transactions on Instrumentation and Measurement, Vol. 69, No. 10, pp. 8064-8075. https://doi.org/10.1109/tim.2020.2992829
  13. Pestana-Viana, D., Zambrano-Lopez, R., de Lima, A. A., Prego, T. D. M., Netto, S. L. and da Silva, E. A.(2016), "The influence of feature vector on the classification of mechanical faults using neural networks", In 2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS), IEEE, pp. 115-118.
  14. Qiao, M., Yan, S., Tang, X. and Xu, C.(2020), "Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads", IEEE Access, Vol. 8, pp. 66257-66269. https://doi.org/10.1109/access.2020.2985617
  15. Refaeilzadeh, P., Tang, L. and Liu, H.(2009), "Cross-validation", Encyclopedia of database systems, Vol. 5, pp. 532-538. https://doi.org/10.1007/978-0-387-39940-9_565
  16. Riaz, S., Elahi, H., Javaid, K. and Shahzad, T.(2017), "Vibration feature extraction and analysis for fault diagnosis of rotating machinery- a literature survey", Asia Pacific Journal of Multidisciplinary Research, Vol. 5, No. 1, pp. 103-110.
  17. Tao, H., Wang, P., Chen, Y., Stojanovic, V. and Yang, H.(2020), "An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks", Journal of the Franklin Institute, Vol. 357, No. 11, pp. 7286-7307. https://doi.org/10.1016/j.jfranklin.2020.04.024
  18. Verstraete, D., Ferrada, A., Droguett, E. L., Meruane, V. and Modarres, M.(2017), "Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings", Shock and Vibration, 2017.
  19. Wang, B., Zhang, X., Xing, S., Sun, C. and Chen, X. (2021), "Sparse representation theory for support vector machine kernel function selection and its application in high-speed bearing fault diagnosis", ISA transactions, Vol. 118, pp. 207-218. https://doi.org/10.1016/j.isatra.2021.01.060
  20. Wang, S. H.(2019), "Partial Discharge Detection in Power Line using 1D Convolutional Neural Network", Hanyang University, Graduate school, Master thesis.
  21. Xing, Z., Qu, J., Chai, Y., Tang, Q. and Zhou, Y.(2017), "Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine", Journal of Mechanical Science and Technology, Vol. 31, No. 2, pp. 545-553. https://doi.org/10.1007/s12206-017-0107-3
  22. Yuan, Z., Zhang, L., Duan, L. and Li, T.(2018), "Intelligent fault diagnosis of rolling element bearings based on HHT and CNN", In 2018 Prognostics and System Health Management Conference (PHM-Chongqing), IEEE, pp. 292-296.