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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Time-series Anomaly Detection and Classification with Long Short-Term Memory Network on Industrial Manufacturing Systems

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DOI: http://dx.doi.org/10.15439/2023F5263

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 171–181 ()

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Abstract. Modern manufacturing systems collect a huge amount of data which gives an opportunity to apply various Machine Learning (ML) techniques. The focus of this paper is on the detection of anomalous behavior in industrial manufacturing systems by considering the temporal nature of the manufacturing process. Long Short-Term Memory (LSTM) networks are applied on a publicly available dataset called Modular Ice-cream factory Dataset on Anomalies in Sensors (MIDAS), which is created using a simulation of a modular manufacturing system for ice cream production. Two different problems are addressed: anomaly detection and anomaly classification. LSTM performance is analysed in terms of accuracy, execution time, and memory consumption and compared with non-time-series ML algorithms including Logistic Regression, Decision Tree, Random Forest, and Multi-Layer Perceptron. The experiments demonstrate the importance of considering the temporal nature of the manufacturing process in detecting anomalous behavior and the superiority in accuracy of LSTM over non-time-series ML algorithms. Additionally, runtime adaptation of the predictions produced by LSTM is proposed to enhance its applicability in a real system.

References

  1. B. Esmaeilian, S. Behdad, and B. Wang, “The evolution and future of manufacturing: A review,” Journal of manufacturing systems, vol. 39, pp. 79–100, 2016.
  2. A. K. Choudhary, J. A. Harding, and M. K. Tiwari, “Data mining in manufacturing: a review based on the kind of knowledge,” Journal of Intelligent Manufacturing, vol. 20, pp. 501–521, 2009.
  3. T.-c. Fu, “A review on time series data mining,” Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 164–181, 2011.
  4. T. Markovic, M. Leon, B. Leander, and S. Punnekkat, “A modular ice cream factory dataset on anomalies in sensors to support machine learning research in manufacturing systems,” IEEE Access, vol. 11, pp. 29 744–29 758, 2023.
  5. K. Choi, J. Yi, C. Park, and S. Yoon, “Deep learning for anomaly detection in time-series data: review, analysis, and guidelines,” IEEE Access, 2021.
  6. B. Lindemann, B. Maschler, N. Sahlab, and M. Weyrich, “A survey on anomaly detection for technical systems using lstm networks,” Computers in Industry, vol. 131, p. 103498, 2021.
  7. A. A. Cook, G. Mısırlı, and Z. Fan, “Anomaly detection for iot time-series data: A survey,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481–6494, 2019.
  8. S. Schmidl, P. Wenig, and T. Papenbrock, “Anomaly detection in time series: a comprehensive evaluation,” Proceedings of the VLDB Endowment, vol. 15, no. 9, pp. 1779–1797, 2022.
  9. Y. Wang, N. Masoud, and A. Khojandi, “Real-time sensor anomaly detection and recovery in connected automated vehicle sensors,” IEEE transactions on intelligent transportation systems, vol. 22, no. 3, pp. 1411–1421, 2020.
  10. G. Shah and A. Tiwari, “Anomaly detection in iiot: A case study using machine learning,” in Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2018, pp. 295–300.
  11. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
  12. W. Jia, R. M. Shukla, and S. Sengupta, “Anomaly detection using supervised learning and multiple statistical methods,” in 2019 18th IEEE International Conference On Machine Learning and Applications (ICMLA). IEEE, 2019, pp. 1291–1297.
  13. G. Hong and D. Suh, “Supervised-learning-based intelligent fault diagnosis for mechanical equipment,” IEEE Access, vol. 9, pp. 116 147–116 162, 2021.
  14. M.-C. Lee, J.-C. Lin, and E. G. Gan, “Rere: A lightweight real-time ready-to-go anomaly detection approach for time series,” in 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020, pp. 322–327.
  15. P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “Lstm-based encoder-decoder for multi-sensor anomaly detection,” arXiv preprint https://arxiv.org/abs/1607.00148, 2016.
  16. D. Y. Oh and I. D. Yun, “Residual error based anomaly detection using auto-encoder in smd machine sound,” Sensors, vol. 18, no. 5: 1308, 2018.
  17. B. Lindemann, N. Jazdi, and M. Weyrich, “Anomaly detection and prediction in discrete manufacturing based on cooperative lstm networks,” in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2020, pp. 1003–1010.
  18. Y. Zhang, Y. Chen, J. Wang, and Z. Pan, “Unsupervised deep anomaly detection for multi-sensor time-series signals,” IEEE Transactions on Knowledge and Data Engineering, 2021.
  19. C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, and N. V. Chawla, “A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 1409–1416.
  20. H. Zhao, Y. Wang, J. Duan, C. Huang, D. Cao, Y. Tong, B. Xu, J. Bai, J. Tong, and Q. Zhang, “Multivariate time-series anomaly detection via graph attention network,” in 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020, pp. 841–850.
  21. Z. Niu, K. Yu, and X. Wu, “Lstm-based vae-gan for time-series anomaly detection,” Sensors, vol. 20, no. 13, pp. 3738–3750, 2020.
  22. R. Corizzo, M. Ceci, G. Pio, P. Mignone, and N. Japkowicz, “Spatially-aware autoencoders for detecting contextual anomalies in geo-distributed data,” in International conference on discovery science. Springer, 2021, pp. 461–471.
  23. C.-Y. Hsu and W.-C. Liu, “Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing,” Journal of Intelligent Manufacturing, vol. 32, pp. 823–836, 2021.
  24. M. Canizo, I. Triguero, A. Conde, and E. Onieva, “Multi-head cnn–rnn for multi-time series anomaly detection: An industrial case study,” Neurocomputing, vol. 363, pp. 246–260, 2019.
  25. H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang, “Time-series anomaly detection service at microsoft,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019, pp. 3009–3017.
  26. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
  27. B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: perceptron, madaline, and backpropagation,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1415–1442, 1990.
  28. B. Leander, T. Marković, A. Čaušević, T. Lindström, H. Hansson, and S. Punnekkat, “Simulation environment for modular automation systems,” in IECON 2022–48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022, pp. 1–6.
  29. B. Leander, T. Markovic, and M. Leon, “Enhanced simulation environment to support research in modular manufacturing systems,” in IECON 2023–49th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2023, pp. 1–6.
  30. M. A. et al., “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/