Abstract
The transition to Industry 4.0 provoked a transformation of industrial manufacturing with a significant leap in automation and intelligent systems. This paradigm shift has brought about a mindset that emphasizes predictive maintenance: detecting future failures when current behaviour of industrial processes and machines is thought to be normal. The constant monitoring of industrial equipment produces massive quantities of data that enables the application of machine learning approaches to this task. This study uses deep learning-based models to build a data-driven predictive maintenance framework for the air production unit (APU), a crucial system for the proper functioning of a Metro do Porto train. This public transport system moves thousands of people every day and train failures lead to delays and loss of trust by clients. Therefore, it is essential not only to detect APU failures before they occur to minimize negative impacts, but also to provide explanations for the failure warnings that can aid in decision-making processes. We propose an autoencoder architecture trained with an adversarial loss, known as the Wasserstein Autoencoder with Generative Adversarial Network (WAE-GAN), designed to detect sensor failures in systems connected to the APU. Our model can detect APU failures up to two hours before they occur, allowing timely intervention of the maintenance teams. We further augment our model with an explainability layer, by providing explanations generated by a rule-based model that focuses on rare events. Results show that our model is able to detect APU failures without any false alarms, fulfilling the requisites of Metro do Porto for early detection of the failures.
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References
Aminian, E., Ribeiro, R.P., Gama, J.: Chebyshev approaches for imbalanced data streams regression models. Data Min. Knowl. Disc. 35(6), 2389–2466 (2021). https://doi.org/10.1007/s10618-021-00793-1
An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1), 1–18 (2015)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Chauhan, S., Vig, L.: Anomaly detection in ECG time signals via deep long short-term memory networks. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–7. IEEE (2015)
Choi, K., Yi, J., Park, C., Yoon, S.: Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access 9, 120043–120065 (2021). https://doi.org/10.1109/ACCESS.2021.3107975
Darban, Z.Z., Webb, G.I., Pan, S., Aggarwal, C.C., Salehi, M.: Deep learning for time series anomaly detection: a survey (2022). https://doi.org/10.48550/arXiv.2211.05244
Davari, N., Veloso, B., Ribeiro, R.P., Gama, J.: Fault forecasting using data-driven modeling: a case study for metro do Porto data set. In: Koprinska, I., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol. 1753, pp. 400–409. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-23633-4_26
Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10 (2021). https://doi.org/10.1109/DSAA53316.2021.9564181
Duarte, J., Gama, J., Bifet, A.: Adaptive model rules from high-speed data streams. ACM Trans. Knowl. Disc. Data (TKDD) 10(3), 1–22 (2016)
Essien, A., Giannetti, C.: A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders. IEEE Trans. Industr. Inf. 16(9), 6069–6078 (2020). https://doi.org/10.1109/TII.2020.2967556
Geiger, A., Liu, D., Alnegheimish, S., Cuesta-Infante, A., Veeramachaneni, K.: TadGAN: time series anomaly detection using generative adversarial networks. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 33–43. IEEE (2020)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703–716. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30490-4_56
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: Lstm-based encoder-decoder for multi-sensor anomaly detection (2016). https://doi.org/10.48550/arXiv.1607.00148
Mobley, R.K.: An introduction to predictive maintenance. Elsevier (2002)
Ng, A., et al.: Sparse autoencoder. CS294A Lect. Notes. 72(2011), 1–19 (2011)
Nguyen, H.D., Tran, K.P., Thomassey, S., Hamad, M.: Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management. Int. J. Inf. Manage. 57, 102282 (2021). https://doi.org/10.1016/j.ijinfomgt.2020.102282
Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)
Ribeiro, R.P., Mastelini, S.M., Davari, N., Aminian, E., Veloso, B., Gama, J.: Online anomaly explanation: a case study on predictive maintenance. In: Koprinska, I., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol. 1753, pp. 383–399. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-23633-4_25
Ribeiro, R.P., Pereira, P., Gama, J.: Sequential anomalies: a study in the railway industry. Mach. Learn. 105, 127–153 (2016)
Sachs, J., Kroll, C., Lafortune, G., Fuller, G., Woelm, F.: Sustainable Development Report 2022. Cambridge University Press, Cambridge (2022)
Said Elsayed, M., Le-Khac, N.A., Dev, S., Jurcut, A.D.: Network anomaly detection using LSTM based autoencoder. In: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, pp. 37–45 (2020). https://doi.org/10.1145/3416013.3426457
Samal, K.K.R., Babu, K.S., Das, S.K.: Temporal convolutional denoising autoencoder network for air pollution prediction with missing values. Urban Clim. 38, 100872 (2021)
Serradilla, O., Zugasti, E., Rodriguez, J., Zurutuza, U.: Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects. Appl. Intell. 52(10), 10934–10964 (2022)
Thill, M., Konen, W., Wang, H., Bäck, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl. Soft Comput. 112, 107751 (2021)
Tolstikhin, I., Bousquet, O., Gelly, S., Schoelkopf, B.: Wasserstein auto-encoders. arXiv preprint arXiv:1711.01558 (2017)
Tukey, J.W., et al.: Exploratory Data Analysis, vol. 2. Reading, MA (1977)
Veloso, B., Gama, J., Ribeiro, R., Pereira, P.: MetroPT2: A Benchmark dataset for predictive maintenance, July 2022. https://doi.org/10.5281/zenodo.7766691
Veloso, B., Ribeiro, R.P., Gama, J., Pereira, P.M.: The metropt dataset for predictive maintenance. Sci. Data 9(1), 764 (2022). https://doi.org/10.1038/s41597-022-01877-3
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103 (2008)
Xu, J., Duraisamy, K.: Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics. Comput. Methods Appl. Mech. Eng. 372, 113379 (2020)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Zonta, T., Da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the industry 4.0: a systematic literature review. Comput. Indus. Eng. 150, 106889 (2020)
Acknowledgements
Miguel E. P. Silva is financed by National Funds through the Portuguese funding agency, FCT - within project UIDP/50014/2020. João Gama is financed by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952026 (HumanE-AI-Net project). We also would like to acknowledge CHIST-ERA-19-XAI-012 and CHIST-ERA/0004/2019.
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Silva, M.E.P., Veloso, B., Gama, J. (2023). Predictive Maintenance, Adversarial Autoencoders and Explainability. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_16
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