References
- Azadeh, A., Asadzadeh, S. M., Salehi, N., & Firoozi, M. (2015). Condition-based maintenance effectiveness for series-parallel power generation system – A combined Markovian simulation model. Reliability Engineering & System Safety, 142, 357–368. https://doi.org/10.1016/j.ress.2015.04.009
- Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150–172. https://doi.org/10.1016/j.ymssp.2014.10.014
- Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751–1760. https://doi.org/10.1016/j.engappai.2013.02.006
- Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
- Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165
- Cubillo, A., Perinpanayagam, S., & Esperon-Miguez, M. (2016). A review of physicsbased models in prognostics: Application to gears and bearings of rotating machinery. Advances in Mechanical Engineering, 8(8), 1–21. https://doi.org/10.1177/1687814016664660
- De Oña, R., Eboli, L., & Mazzulla, G. (2014). Key factors affecting rail service quality in Northern Italy: A decision tree approach. Transport, 29(1), 75–83. https://doi.org/10.3846/16484142.2014.898216
- DeCastro, J. A., Litt, J. S., & Frederick, D. K. (2008). A modular aero-propulsion system simulation of a large commercial aircraft engine. 44th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit. https://doi.org/10.2514/6.2008-4579
- Elsheikh, A., Yacout, S., & Ouali, M. S. (2019). Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing, 323, 148–156. https://doi.org/10.1016/j.neucom.2018.09.076
- Frederick, D. K., DeCastro, J. A., & Litt, J. S. (2007). User’s guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). NASA Technical Reports Server (NTRS). https://ntrs.nasa.gov/citations/20070034949
- Gebraeel, N., Lawley, M., Liu, R., & Parmeshwaran, V. (2004). Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Reliability, 51(3), 694–700.
- Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017). Convolutional sequence to sequence learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 1), 562–570. https://arxiv.org/abs/1705.03122
- Gonzalez, T. F. (2007). Handbook of approximation algorithms and metaheuristics. Chapman and Hall/CRC. https://doi.org/10.1201/9781420010749
- Heimes, F. O. (2008). Recurrent neural networks for remaining useful life estimation. 2008 International Conference on Prognostics and Health Management (PHM 2008). https://doi.org/10.1109/PHM.2008.4711422
- Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges, and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739. https://doi.org/10.1016/j.ymssp.2008.06.007
- Johnson, R., & Zhang, T. (2017). Deep pyramid convolutional neural networks for text categorization. ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 1), 562–570. https://doi.org/10.18653/v1/P17-1052
- Jouin, M., Gouriveau, R., Hissel, D., Péra, M. C., & Zerhouni, N. (2016). Degradations analysis and aging modeling for health assessment and prognostics of PEMFC. Reliability Engineering & System Safety, 148, 78–95. https://doi.org/10.1016/j.ress.2015.12.003
- Kali, Y., & Linn, M. (2010). Science. In International Encyclopedia of Education (3rd ed., pp. 468–474). https://doi.org/10.1016/B978-0-08-044894-7.00081-6
- Krizhevsky, B. A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.
- Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems – Reviews, methodology, and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. https://doi.org/10.1016/j.ymssp.2013.06.004
- Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016
- Li, Y., Zhang, S., Wang, X., Liang, B., & Lu, W. (2019). Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews, 113, Article 109254. https://doi.org/10.1016/j.rser.2019.109254
- Liao, L., & Köttig, F. (2014). Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 63(1), 191–207. https://doi.org/10.1109/TR.2014.2299152
- Liao, L., Jin, W., & Pavel, R. (2016). Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment. IEEE Transactions on Industrial Electronics, 63(11), 7076–7083. https://doi.org/10.1109/TIE.2016.2586442
- Navathe, S. B., Wu, W., Shekhar, S., Du, X., Wang, X. S., & Xiong, H. (2016). Database systems for advanced applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16–19, 2016, Proceedings, Part I. Springer. https://doi.org/10.1007/978-3-319-32025-0
- Pecht, M., & Gu, J. (2009). Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31(3–4), 309–322. https://doi.org/10.1177/0142331208092031
- Qian, Y., Yan, R., & Gao, R. X. (2017). A multi-time scale approach to remaining useful life prediction in rolling bearing. Mechanical Systems and Signal Processing, 83, 549– 567. https://doi.org/10.1016/j.ymssp.2016.06.031
- Rezaeian Jouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement: Journal of the International Measurement Confederation, 163, Article 107929. https://doi.org/10.1016/j.measurement.2020.107929
- Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. 2008 International Conference on Prognostics and Health Management (PHM 2008). https://doi.org/10.1109/PHM.2008.4711414
- Shenfield, A., & Howarth, M. (2020). A novel deep learning model for the detection and identification of rolling element-bearing faults. Sensors (Switzerland), 20(18), Article 5112. https://doi.org/10.3390/s20185112
- Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation – A review on the statistical data-driven approaches. European Journal of Operational Research, 213(1), 1–14.
- Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modeling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836. https://doi.org/10.1016/j.ymssp.2010.11.018
- Sishi, M., & Telukdarie, A. (2021). The application of decision tree regression to optimize business processes. Proceedings of the International Conference on Industrial Engineering and Operations Management (No. Dm), 48–57.
- Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005). Decision tree classifier for network intrusion detection with GA-based feature selection. Proceedings of the Annual Southeast Conference (Vol. 2), 2136–2141. https://doi.org/10.1145/1167253.1167288
- Suah, F. B. M. (2017). Preparation and characterization of a novel Co(II) optode based on polymer inclusion membrane. Analytical Chemistry Research, 12, 40–46. https://doi.org/10.1016/j.ancr.2017.02.001
- Tian, Z. (2012). An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing, 23(2), 227–237. https://doi.org/10.1007/s10845-009-0356-9
- Tian, Z., Wong, L., & Safaei, N. (2010). A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing, 24(5), 1501–1514.
- Xiao, H., Yuan, K., & Zhan, Z. (2022). System reliability analysis based on dependent Kriging predictions and parallel learning strategy. Reliability Engineering & System Safety, 218, Article 108198. https://doi.org/10.1016/j.ress.2022.108198
- Yan, R., Ma, Z., Zhao, Y., & Kokogiannakis, G. (2016). A decision tree-based data-driven diagnostic strategy for air handling units. Energy and Buildings, 133, 37–45. https://doi.org/10.1016/j.enbuild.2016.09.039
- Zhang, S., Peng, H., Fu, J., Lu, Y., & Luo, J. (2021). Multi-scale 2D temporal adjacency networks for moment localization with natural language. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2021.3120745
- Zhao, Z., Liang, B., Wang, X., & Lu, W. (2017). Remaining useful life prediction of aircraft engine based on degradation pattern learning. Reliability Engineering & System Safety, 164(457), 74–83. https://doi.org/10.1016/j.ress.2017.02.007