Adadi, A., & Berrada, M. (2018). Peeking inside the black box: a survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. Aghababaeyan, Z., Abdellatif, M., Briand, L., Ramesh, S., & Bagherzadeh, M. (2023). Black-box testing of deep neural networks through test case diversity. IEEE Transactions on Software Engineering, 49(5), 3182–3204. Amin, O., Brown, B., Stephen, B., & McArthur, S. (2022, June). A case-study-led investigation of explainable AI (XAI) to support the deployment of prognostics in industry. In Proceedings of the European Conference Of The PHM Soci-ety 2022 (pp. 9–20). Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021). Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1424. Arsenault, P. D., Wang, S., & Patenaude, J. M. (2025). A survey of explainable artificial intelligence (XAI) in financial time series forecasting. ACM Computing Surveys, 57(10), 1–37. Awotunde, J. B., Jimoh, R. G., Adeniyi, A. E., Ayo, E. F., Ajamu, G. J., & Aremu, D. R. (2023). Application of Interpret-able Artificial Intelligence Enabled Cognitive Internet of Things for COVID-19 Pandemics. In Explainable Machine Learning for Multimedia-Based Healthcare Applications (pp. 191–213). Cham: Springer International Publishing. Ayeni, O. (2025). Integration of Artificial Intelligence in predictive maintenance for mechanical and industrial engi-neering. International Research Journal of Modernisation in Engineering Technology and Science, 7(3), 1-23. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K. R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7), e0130140. Belle, V., & Papantonis, I. (2020). Principles and Practice of Explainable Machine Learning. arXiv:2009.11698. Bobek, S., Kuk, M., Szelążek, M., & Nalepa, G. J. (2022). Enhancing cluster analysis with explainable AI and multidi-mensional cluster prototypes. IEEE Access, 10, 101556–101574. doi:10.1109/ACCESS.2022.3208957. Bobek, S., Nowaczyk, S., Gama, J., Pashami, S., Ribeiro, R. P., Taghiyarrenani, Z., ... & Nalepa, G. J. (2023). Why In-dustry 5.0 Needs XAI 2.0? non Bothra, R., Arun, V., Godfrey, B., Narayan, A., & Saeed, A. (2024, November). Lightweight Automated Reasoning for Network Architectures. In Proceedings of the 23rd ACM Workshop on Hot Topics in Networks (pp. 237–245). Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245–317. Cerquitelli, T., Nikolakis, N., Morra, L., Bellagarda, A., Orlando, M., Salokangas, R., ... & Macii, E. (2021). Data-driven predictive maintenance: A methodology primer. In Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-cases (pp. 39–73). Singapore: Springer Singapore. Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023). A review of trustworthy and ex-plainable artificial intelligence (XAI). IEEE Access. Djeddi, A. (2024). Maintenance and Reliability. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64, 86–92. Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Spectre, M., & Kagal, L. (2018). Explaining Explanations: An Overview of Interpretability of Machine Learning. In 2018, IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 80–89). doi:10.1109/DSAA. 2018.00018. Hajgató, G., Wéber, R., Szilágyi, B., Tóthpál, B., Gyires-Tóth, B., & Hős, C. (2022). Predmax: Predictive maintenance with explainable deep convolutional autoencoders. Advanced Engineering Informatics, 54, 101778. URL:https://www.sciencedirect.com/science/article/pii/S1474034622002361. doi https://doi.org/10.1016/j.aei.2022.101778. Haque, A. K. M. (2025). Explainable artificial intelligence (XAI): making AI understandable for end users. Hart, S. (1989). Shapley value. In Game Theory (pp. 210–216). London: Palgrave Macmillan UK. Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., ... & Hussain, A. (2024). Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation, 16(1), 45–74. Hitchcock, L. (2006, July). ISO standards for condition monitoring. In Engineering Asset Management: Proceedings of the 1st World Congress on Engineering Asset Management (WCEAM) 11–14 July 2006 (pp. 606–613). London: Springer London. Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Muller, H. (2019). Causality and explainability of artificial intelli-gence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov., 9, 1 – 13. Hrnjica, B., & Softic, S. (2020, August). Explainable AI in manufacturing: a predictive maintenance case study. In IFIP International Conference on Advances in Production Management Systems (pp. 66–73). Cham: Springer Internation-al Publishing. Infant, S. S., Vickram, A. S., Saravanan, A., Muthu, C. M., & Yuarajan, D. (2025). Explainable artificial intelligence for sustainable urban water systems engineering. Results in Engineering, 104349. Kaufmann, L., & Rousseeuw, P. (1987). Clustering using Medoids. Data Analysis based on the L1-Norm and Related Methods, (pp. 405–416). Kaur, K., Selway, M., Grossmann, G., Stumptner, M., & Johnston, A. (2018, October). Towards an open-standards-based framework for achieving condition-based predictive maintenance. In Proceedings of the 8th International Conference on the Internet of Things (pp. 1–8). Kim, B., Khanna, R., & Koyejo, O. O. (2016). Examples are not enough; learn to criticise! Criticism for interpretability. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in Neural Information Processing Sys-tems. Curran Associates, Inc., volume 29. URL: https://proceedings.neurips.cc/paper/2016/file/5680522b8e2bb01943234bce7bf84534-Paper.pdf. Kisten, M., Ezugwu, A. E., & Olusanya, M. S. (2024). Explainable Artificial Intelligence Model for Predictive Mainte-nance in Smart Agricultural Facilities. IEEE Access. Krishnamurthy, V., Nezafati, K., Stayton, E., & Singh, V. (2020). Explainable AI framework for imaging-based predic-tive maintenance for automotive applications and beyond. Data-Enabled Discovery and Applications, 4(1), 7. Kuk, M., Bobek, S., Veloso, B., Rajaoarisoa, L., & Nalepa, G. J. (2023, June). Feature importance as a tool for root-cause analysis of time-series events. In International Conference on Computational Science (pp. 408–416). Cham: Springer Nature Switzerland. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H.Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Pro-cessing Systems 30 (pp. 4765–4774). Curran Associates, Inc. Mahale, Y., Kolhar, S., & More, A. S. (2025). A comprehensive review on artificial intelligence-driven predictive maintenance in vehicles: technologies, challenges and future research directions. Discover Applied Sciences, 7(4), 243. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267, 1–38. Moraffah, R., Karami, M., Guo, R., Raglin, A. J., & Liu, H. (2020). Causal interpretability for machine learning - prob-lems, methods, and evaluation. ACM SIGKDD Explorations Newsletter, 22, 18 –33. Nor, A. K. M., Pedapati, S. R., Muhammad, M., & Leiva, V. (2022). Abnormality detection and failure prediction using explainable Bayesian deep learning: Methodology and case study with industrial data: Mathematics, 10(4), 554. Pashami, S., Nowaczyk, S., Fan, Y., Jakubowski, J., Paiva, N., Davari, N., ... & Gama, J. (2023). Explainable predictive maintenance. arXiv preprint arXiv:2306.05120. Rajaoarisoa, L., Randrianandraina, R., Nalepa, G. J., & Gama, J. (2025). Improving decision-making systems based on explainable artificial intelligence approaches for predictive maintenance. Engineering Applications of Artificial In-telligence, 139, 109601. Rane, J., Mallick, S. K., Kaya, O., & Rane, N. L. (2024). Enhancing black-box models: advances in explainable artificial intelligence for ethical decision-making. Future Research Opportunities for Artificial Intelligence in Industry 4.0 and 5.2. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’16 (p. 1135–1144). New York, NY, USA: Association for Computing Machinery. doi:10.1145/2939672.2939778. Rojas, L., Peña, Á., & Garcia, J. (2025). AI-driven predictive maintenance in mining: a systematic literature review on fault detection, digital twins, and intelligent asset management. Applied Sciences, 15(6), 3337. Shukla, B., Fan, I. S., & Jennions, I. (2020, July). Opportunities for Explainable Artificial Intelligence in Aerospace Pre-dictive Maintenance. In PHM Society European Conference (Vol. 5, No. 1, pp. 11–11). Verma, S., Lahiri, A., Dickerson, J. P., & Lee, S. I. (2021). Pitfalls of explainable ML: an industry perspective. arXiv preprint arXiv:2106.07758. Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for prognostics and health management (PHM) tech-niques within manufacturing operations. Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Auto-mated decisions and the GDPR. Harv. JL & Tech., 31, 841. Wang, Y. (2024). A comparative analysis of model agnostic techniques for explainable artificial intelligence. Research Reports on Computer Science, 25–33. XAI's current state-of-the-art pitfalls: Zeldam, S. G. (2018). Automated failure diagnosis in aviation maintenance using explainable artificial intelligence (XAI) (Master's thesis, University of Twente). Zemmouchi-Ghomari, L. (2023, April). Ontology and machine learning: a two-way street to improved knowledge repre-sentation and algorithm accuracy. In International Conference on Paradigms of Communication, Computing and Da-ta Analytics (pp. 181–189). Singapore: Springer Nature Singapore. Zeng, Z., Cheng, Q., & Si, Y. (2023). Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey. Ma-thematics, 11(21), 4486. Zhang, X., Fujiwara, T., Chandrasegaran, S., Brundage, M. P., Sexton, T., Dima, A., & Ma, K.-L. (2021). A visual ana-lytics approach for diagnosing heterogeneous and multidimensional machine maintenance data. In 2021, IEEE 14th Pacific Visualisation Symposium (PacificVis) (pp.196–205). doi:10.1109/PacificVis52677.2021.00033.