Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Anomaly Detection System in Mechanical Facility Equipment: Using Long Short-Term Memory Variational Autoencoder

LSTM-VAE를 활용한 기계시설물 장치의 이상 탐지 시스템

  • 서재홍 (연세대학교 산업공학과) ;
  • 박준성 (연세대학교 산업공학과) ;
  • 유준우 (연세대학교 산업공학과) ;
  • 박희준 (연세대학교 산업공학과)
  • Received : 2021.11.25
  • Accepted : 2021.12.01
  • Published : 2021.12.31
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Abstract

Purpose: The purpose of this study is to compare machine learning models for anomaly detection of mechanical facility equipment and suggest an anomaly detection system for mechanical facility equipment in subway stations. It helps to predict failures and plan the maintenance of facility. Ultimately it aims to improve the quality of facility equipment. Methods: The data collected from Daejeon Metropolitan Rapid Transit Corporation was used in this experiment. The experiment was performed using Python, Scikit-learn, tensorflow 2.0 for preprocessing and machine learning. Also it was conducted in two failure states of the equipment. We compared and analyzed five unsupervised machine learning models focused on model Long Short-Term Memory Variational Autoencoder(LSTM-VAE). Results: In both experiments, change in vibration and current data was observed when there is a defect. When the rotating body failure was happened, the magnitude of vibration has increased but current has decreased. In situation of axis alignment failure, both of vibration and current have increased. In addition, model LSTM-VAE showed superior accuracy than the other four base-line models. Conclusion: According to the results, model LSTM-VAE showed outstanding performance with more than 97% of accuracy in the experiments. Thus, the quality of mechanical facility equipment will be improved if the proposed anomaly detection system is established with this model used.

Keywords

References

  1. Akcay S, Atapour-Abarghouei A. Breckon T P. 2019. Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. 2019 International Joint Conference on Neural Networks (IJCNN). IEEE.
  2. An J. and Cho S. 2015. Variational Autoencoder based AnomalyDetection using Reconstruction Probability. Technical Report. SNU Data MiningCenter. 1-18.
  3. Bank D., Koenigstein N., and Giryes R. 2020. Autoencoders. arXiv eprints.
  4. Baur C., Wiestler B., Albarqouni S., and Navab N. 2018. Deep autoencoding models for unsupervised anomaly seg- mentation in brain MR images. In Proceeding of International MICCAI Brainlesion Workshop. 11383:161-169.
  5. Chalapathy R. and Chawla S. 2019. Deep Learning for Anomaly Detection: A Survey. CoRR, (abs/1901.03407).
  6. Chandola V., Banerjee A., and Kumar V. 2009. Anomaly detection : A survey, ACM computing surveys (CSUR), 41(3):15.
  7. DataRPM, P. 2017. Anomaly Detection & Prediction Decoded: 6 Industries. Copious Challenges. Extraordinary Impact. Technical Report.
  8. E.S. Lee, H.C. Bae, H.J. Kim, H.N. Han, Y.K., and Lee, J.Y. Son. 2020. Trends in AI Technology for Smart Manufacturing in the Future. Electronics and Telecommunications Trends. 35(1):60-70. https://doi.org/10.22648/ETRI.2020.J.350106
  9. Guo Y., Liao W., Wang Q., Yu L., Ji T., and Li P. 2018. Multidimensional Time Series Anomaly Detection: A GRU-based Gaussian Mixture Variational Autoencoder Approach. Proceedings of the 10th Asian Conference on Machine Learning, PMLR. 95:97-112.
  10. Harmeling S., Dornhege G., Tax D., Meinecke F., and Muller K R. 2006. From outliers to prototypes : ordering data. Neurocomputing 69(13-15):1608-1618. https://doi.org/10.1016/j.neucom.2005.05.015
  11. Hasan M., Islam M M, Zarif M I I, and Hashem M M A. 2019. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things. 7:100059 https://doi.org/10.1016/j.iot.2019.100059
  12. Hodge V. and Austin, J. 2004. A survey of outlier detection methodologies. Artificial Intelligence Review 22(2):85-126. https://doi.org/10.1023/B:AIRE.0000045502.10941.a9
  13. J.S. Kim, C.K. Lee, K.N. Kim, C.W. Kim, H.M. Song, S.S. Ahn, J.W. Oh, H.S. Jo, and S.S. Han. 2021. A Case Study on the Target Sampling Inspection for Improving Outgoing Quality. Journal of Korean Society for Quality Management 49(3):421-431. https://doi.org/10.7469/JKSQM.2021.49.3.421
  14. K.H. Sun, H Huh, Tama, B A, S.Y Lee, J.H. Jung, and S. Lee, 2020. Vision-Based Fault Diagnostics Using Explainable Deep Learning with Class Activation Map. IEEE Access 8:129169-129179. https://doi.org/10.1109/access.2020.3009852
  15. Li D, Chen D, Goh J, and Ng S K. 2018. Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758.
  16. M.J. Kim, Y.H. Park, T.K. Kim, J.S. Jung. 2019. A Study on the Prediction Diagnosis System Improvement by Error Terms and Learning Methodologies Application. Journal of Korean Society for Quality Management. 47(4):783-793.
  17. M.K. Seo, and W.Y. Yun. 2019. Condition Monitoring and Diagnosis of a Hot Strip Roughing Mill Using an Autoencoder. Journal of Korean Society for Quality Management 47(1):75-86. https://doi.org/10.7469/JKSQM.2019.47.1.75
  18. Maesschalck R D, Jouan-Rimbaud D, and Massart D L. 2000. The Mahalanobis Distance. Chemometrics and Intelligent Laboratory Systems 50(1):1-18. https://doi.org/10.1016/S0169-7439(99)00047-7
  19. Malhotra P, Vig L, Shroff G, and Agarwal P. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings of Presses Universitaires de Louvain 89:89-94.
  20. Munawar A, Vinayavekhin P, and Magistris G. D. 2017. Limiting the reconstruction capability of generative neural network using negative learning. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE.
  21. N.Y. Choi, and W.H. Kim. 2019. Detecting user behavior anomalies using Generative Adversarial Networks. Intelligence Information Research 25(3):43-62.
  22. Park D, Hoshi Y, and Kemp C C. 2018. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder. IEEE Robotics and Automation Letters 3(3):1544-1551. https://doi.org/10.1109/lra.2018.2801475
  23. Perera P, Nallapati R, and Xiang B. 2019. Ocgan: One-class novelty detection using gans with constrained latent representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  24. Pokrajac D, Lazarevic A, and Latecki L. 2007. Incremental Local Outlier Detection for Data Streams. CIDM Conference.
  25. Rousseeuw P. and Leroy A. 2003. Robust Regression and Outlier Detection. Wiley.
  26. Son V H, Daisuke U, Kiyoshi H, Kazuki M, Pranata S, and Shen S. M. 2019. Anomaly detection with adversarial dual autoencoders. arXiv eprint.
  27. Wei Q, Ren Y, Hou R, Shi B, Lo J Y, and Carin L. 2018. Anomaly detection for medical images based on a one-class classification. In Proceeding of Medical Imaging 2018: Computer-Aided Diagnosis.
  28. Yamanaka Y, Iwata T, Takahashi H, Yamada M., and Kanai S. 2019. Autoencoding binary classifiers for supervised anomaly detection. Pacific Rim International Conference on Artificial Intelligence.
  29. Zong B, Song Q, Min M R, Cheng W. Lumezanu C, Cho D, and Chen H. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In Proceeding of International Conference on Learning Representations.