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[The graphic presents 5 logos, in 3 rows. In the top row there is a logo of the XPM Project, which is an eyeball surrounded by a black-green spiral. Second and third rows present logotypes of the project funders. In the second row there are logos of French Agence Nationale de la Recherche and Portugese Fundação para a Ciência e a Tecnologia. The lowest row contains logos of Polish Narodowe Centrum Nauki and Swedish Vetenskapsrådet.]

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Results of the Project

Click on the category of results you are interested in.

  1. Sousa Tomé, E., Ribeiro, R. P., Dutra, I., & Rodrigues, A. (2023). An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems. Sensors, 23(10), 4902. https://doi.org/10.3390/s23104902
  2. Gama, J., Ribeiro, R. P., & Veloso, B. (2022). Data-Driven Predictive Maintenance. IEEE Intelligent Systems, 37(4): 27-29. https://doi.org/10.1109/MIS.2022.3167561
  3. Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G.J. (2022). Anomaly Detection in Asset Degradation Process using Variational Autoencoder and Explanations. Sensors, 22(1), 291. https://doi.org/10.3390/s22010291
  4. Veloso, B., Ribeiro, R. P., Gama, J., & Pereira, P. M. (2022). The MetroPT dataset for predictive maintenance. Scientific Data, 9(1), 764. https://doi.org/10.1038/s41597-022-01877-3
  5. Davari, N., Veloso, B., Costa, G.D.A., Pereira, P.M., Ribeiro, R.P., & Gama, J. (2021). A Survey on Data-Driven Predictive Maintenance for the Railway Industry. Sensors, 21(17), 5739. https://doi.org/10.3390/s21175739

  1. Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G.J. (2023). Towards Online Anomaly Detection in Steel Manufacturing Process. ICCS 2023. [forthcoming]
  2. Kuk, M., Bobek, S., Veloso, B., Rajaoarisoa, L., & Nalepa, G.J. (2023). Feature Importances as a Tool for Root Cause Analysis in Time-series Events. ICCS 2023. [forthcoming]
  3. Kuk, E., Bobek, S., & Nalepa, G.J. (2023). ML-based Proactive Control of Industrial Processes. ICCS 2023. [forthcoming]
  4. Berenji, A., Taghiyarrenani, Z., & Nowaczyk, S. (2023). curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection. In I. Koprinska et al. (Eds.). ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 423-437). Cham: Springer. https://doi.org/10.1007/978-3-031-23633-4_28
  5. Davari, N., Veloso, B., Ribeiro, R.P., Gama, J. (2023). Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. In I. Koprinska et al. (Eds.). ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 400-409). Cham: Springer. https://doi.org/10.1007/978-3-031-23633-4_26
  6. Fan, Y., Sarmadi, H., Nowaczyk, S. (2023). Incorporating Physics-Based Models into Data Driven Approaches for Air Leak Detection in City Buses. In I. Koprinska et al. (Eds.). ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 438-450). Cham: Springer. https://doi.org/10.1007/978-3-031-23633-4_29
  7. Jamshidi, S., Nowaczyk, S., Fanaee-T, H., & Rahat, M. (2023). A Systematic Approach for Tracking the Evolution of XAI as a Field of Research. In I. Koprinska et al. (Eds.). ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 461-476). Cham: Springer. https://doi.org/10.1007/978-3-031-23633-4_31
  8. Ribeiro, R.P., Mastelini, S.M., Davari, N., Aminian, E., Veloso, B., & Gama, J. (2023). Online Anomaly Explanation: A Case Study on Predictive Maintenance. In I. Koprinska et al. (Eds.). ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 383-399). Cham: Springer. https://doi.org/10.1007/978-3-031-23633-4_25
  9. Sousa Tomé, E., Ribeiro, R.P., Veloso, B., & Gama, J. (2023). An Online Data-Driven Predictive Maintenance Approach for Railway Switches. In I. Koprinska et al. (Eds.). ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 410-422). Cham: Springer. https://doi.org/10.1007/978-3-031-23633-4_27
  10. Alabdallah, A., Pashami, S., Rögnvaldsson, T., & Ohlsson, M. (2022). SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE. https://doi.org/10.1109/DSAA54385.2022.10032392
  11. Jakubowski, J., Stanisz, P., Bobek, S., Nalepa, G.J. (2022). Performance of Explainable AI Methods in Asset Failure Prediction, In D. Groen, C. de Mulatier, M. Paszynski, V.V. Krzhizhanovskaya, J.J. Dongarra, & P.M.A. Sloot (Eds.). Computational Science – ICCS 2022 (pp. 472-485). Cham: Springer.  https://doi.org/10.1007/978-3-031-08760-8_40 
  12. Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G.J. (2022). Roll Wear Prediction in Strip Cold Rolling with Physics-Informed Autoencoder and Counterfactual Explanations. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE. https://doi.org/10.1109/DSAA54385.2022.10032357
  13. Davari, N., Pashami, S., Veloso, B., Nowaczyk, S., Fan, Y., Pereira, P.M., Ribeiro, R.P., & Gama, J. (2022). A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. In T. Bouadi, E. Fromont, E. Hüllermeier (Eds.). Advances in Intelligent Data Analysis XX. IDA 2022 (pp. 39-52). Cham: Springer.  https://doi.org/10.1007/978-3-031-01333-1_4 
  14. Kuk, M., Bobek, S., & Nalepa, G.J. (2022). Comparing Explanations from Glass-Box and Black-Box Machine-Learning Models. In D. Groen, C. de Mulatier, M. Paszynski, V.V. Krzhizhanovskaya, J.J. Dongarra, & P.M.A. Sloot (Eds.). Computational Science – ICCS 2022 (pp. 668-675). Cham: Springer. https://doi.org/10.1007/978-3-031-08757-8_55
  15. del Moral, P., Nowaczyk, S., & Pashami, S. (2022). Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models. In P. Do, G. Michau, & C. Ezhilarasu (Eds.). Proceedings of the 7th European Conference of the Prognostics and Health Management Society (pp. 110-117). PHM Society. https://doi.org/10.36001/phme.2022.v7i1.3360
  16. Sant’Ana, B., Veloso, B., & Gama, J. (2022). Predictive maintenance for wind turbines. In Technologies, Markets and Policies: Bringing Together Economics and Engineering. 5th International Conference on Energy and Environment Proceedings (pp. 416-421). Porto: Univ. Porto.
  17. Bobek, S., Nalepa, G.J. (2021). Introducing Uncertainty into Explainable AI Methods. In M. Paszynski, D. Kranzlmüller, V.V. Krzhizhanovskaya, J.J. Dongarra, P.M.A. Sloot (Eds.). Computational Science – ICCS 2021 (pp. 444-457). Cham: Springer. https://doi.org/10.1007/978-3-030-77980-1_34
  18. Bobek, S., Bałaga, P., Nalepa, G.J. (2021). Towards Model-Agnostic Ensemble Explanations. In M. Paszynski, D. Kranzlmüller, V.V. Krzhizhanovskaya, J.J. Dongarra, P.M.A. Sloot (Eds.). Computational Science – ICCS 2021 (pp. 39-51). Cham: Springer. https://doi.org/10.1007/978-3-030-77970-2_4
  19. Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., & Gama, J., (2021). Predictive maintenance based on anomaly detection using deep learning for air production units in the railway industry. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE. https://doi.org/10.1109/DSAA53316.2021.9564181
  20. Jakubowski, J., Stanisz, P., Bobek, S. & Nalepa, G.J. (2021). Explainable anomaly detection for Hot-rolling industrial process. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE. https://doi.org/10.1109/DSAA53316.2021.9564228
  21. Pereira, B.S.R. (2021). Fault Detection in Wind Turbines. [Master Thesis in Data Analytics], Univ. Porto. https://repositorio-aberto.up.pt/handle/10216/136624.