Trends and Challenges of Machine Learning-Based Predictive Maintenance in Aviation Industry
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7775-8_39
Reference13 articles.
1. J. Bzai et al., “Machine Learning-Enabled Internet of Things (IoT): Data, Applica tions, and Industry Perspective,” Electronics, vol. 11, no. 17, p. 2676, 2022, doi: https://doi.org/10.3390/electronics11172676.
2. G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, “Machine Learning for Predictive Maintenance: A Multiple Classifier Approach,” IEEE Trans. Ind. Inform., vol. 11, no. 3, pp. 812–820, 2015, doi: https://doi.org/10.1109/TII.2014.2349359.
3. G. G. Samatas, S. S. Moumgiakmas, and G. A. Papakostas, “Predictive Maintenance - Bridging Artificial Intelligence and IoT,” in 2021 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA: IEEE, May 2021, pp. 0413–0419. doi: https://doi.org/10.1109/AIIoT52608.2021.9454173.
4. P. Adhikari and H. G. Rao, “Machine Learning based Data Driven Diagnostics & Prognostics Framework for Aircraft Predictive Maintenance”.
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