E3S Web Conf.
Volume 312, 202176th Italian National Congress ATI (ATI 2021)
|Number of page(s)||17|
|Published online||22 October 2021|
- Kang, Z., Catal, C. and Tekinerdogan, B. “Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks.”, Sensors, vol. 21, 932, 2011. [Google Scholar]
- Wang, T. “Trajectory Similarity Based Prediction for Remaining Useful Life Estimation.”, 2010. [Google Scholar]
- Wang, T., Yu, J., & Siegel, D., and Lee, J. “A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems.”, 1–6. 10.1109/PHM.2008.4711421, 2008. [Google Scholar]
- Heimes, F. “Recurrent neural networks for remaining useful life estimation.”, 1–6. 10.1109/PHM.2008.4711422, 2008. [Google Scholar]
- Li, X., Ding, Q., and Sun, J.Q. “Remaining Useful Life Estimation in Prognostics Using Deep Convolution Neural Networks.”, Reliability Engineering & System Safety. 172. 10.1016/j.ress.2017.11.021, 2017. [Google Scholar]
- Yuan, M., Wu, Y. and Lin, L. “Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network.”, 2016 IEEE International Conference on Aircraft Utility Systems (AUS), pp. 135–140, 2016. [Google Scholar]
- Taha, H., Sakr, A., and Yacout, S. “Aircraft Engine Remaining Useful Life Prediction Framework for Industry 4.0”, 2019. [Google Scholar]
- Bakir, A., Zaman, M., Hassan, A., and Hamid, M. “Prediction of remaining useful life for mech equipment using regression.”, Journal of Physics: Conference Series. 1150. 012012. 10.1088/1742-6596/1150/1/012012, 2019. [Google Scholar]
- Aye, S.A. and Heyns, S. “An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission.”, Mechanical Systems and Signal Processing. 84. pp. 485498. 10.1016/j.ymssp.2016.07.039, 2017. [Google Scholar]
- Liu, J. and Chen, Z. “Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Health Indicator and Gaussian Process Regression Model”, IEEE Access, vol. 7, pp. 39474–39484, 2019. [Google Scholar]
- Saxena, A., Goebel, K., Simon, D., and Eklund, N. “Damage propagation modeling for aircraft engine run-to-failure simulation.”, 2008 International Conference on Prognostics and Health Management, pp. 1–9, 2008. [Google Scholar]
- Zhao, C., Huang, X., Li, Y., Yousaf Iqbal, M. “A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction”, Sensors, vol. 20, 7109, 2020. [Google Scholar]
- Peng, C., Chen, Y., Chen, Q., Tang, Z., Li, L., Gui, W., (2021). “A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion”, Sensors, vol. 21, 418, 2021. [Google Scholar]
- Molnar, C. “Interpretable machine learning. A Guide for Making Black Box Models Explainable”, https://christophm.github.io/interpretable-ml-book/, p.49, 2018. [Google Scholar]
- Wang, J. “An Intuitive Tutorial to Gaussian Processes Regression”, 2020. [Google Scholar]
- https://www.heatonresearch.com/2017/06/01/hidden-layers.html [Google Scholar]
- https://www.mathworks.com/help/deeplearning/ref/trainlm.html [Google Scholar]
- Sambasivan, R. and Das, S. “Big Data Regression Using Tree Based Segmentation,” 2017 14th IEEE India Council International Conference (INDICON), pp. 1–6, 2017. [Google Scholar]
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