Explainable Artificial Intelligence Applied to Predictive Maintenance: Comparison of Post-Hoc Explainability Techniques
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63803-9_19
Reference20 articles.
1. 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. Ind. Eng. 150, 106889 (2020). https://doi.org/10.1016/j.cie.2020.106889
2. Florian, E., Sgarbossa, F., Zennaro, I.: Machine learning-based predictive maintenance: a cost-oriented model for implementation. Int. J. Prod. Econ. 236, 108114 (2021). https://doi.org/10.1016/j.ijpe.2021.108114
3. Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., Elger, G.: Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry. Reliab. Eng. Syst. Saf. 215, 107864 (2021). https://doi.org/10.1016/j.ress.2021.107864
4. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052
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