Towards Reliable RUL Prediction Impact of Feature Selection on Degradation Modelling
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Published
Sep 11, 2025
Oluwasegun Oluwole Gbore
Mehak Shafiq
Amit Kumar Jain
Don McGlinchey
Abstract
This study investigates feature selection techniques for predicting the Remaining Useful Life (RUL) of aircraft engines, addressing the persistent challenge of inaccurate predictions due to suboptimal feature selection. In this context, a robust methodology was developed to select optimal features for enhancing the model’s predictive power. Using inferential statistical methods for analysing operation data from aircraft engines, the study involved data pre-processing to test its feasibility, feature engineering to minimise data variability, backward elimination for linear regression, random forest and gradient boosting for effective feature selection. The models’ performance was evaluated for predictive accuracy and reliability using various performance metrics. Findings show that the random forest model with an R-squared value of 0.86 surpassed linear regression (0.76) and gradient boosting (0.73). It further highlighted that the integration of advanced feature selection techniques in non-linear modelling substantially improved the prediction accuracy of RUL while also capturing the essential degradation patterns typical in aircraft engines, as depicted in the Partial Dependence Plots (PDPs). All the three models highlighted the critical importance of the 'time' (current age) feature in predicting RUL, accounting for more than half of the model's predictive power. The findings of this work not only supported some initial hypotheses regarding sensor relationships and operational settings' effects but also unveiled complex interactions previously unrecognized. By identifying and eliminating redundant sensors though a systematic approach of feature selection, this study significantly contributes to the field of predictive maintenance for aircraft engines in enhancing the robustness of predictive models.
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Keywords
RUL Prediction, Feature Selection, Degradation Modelling, predictive maintenance, aircraft engines
References
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Chinta, V.S., Kethi Reddi, S., & Yarramsetty, N. (2023). Optimal feature selection on Serial Cascaded deep learning for predictive maintenance system in automotive industry with fused optimization algorithm. Advanced Engineering Informatics, [online] 57, p. 102105. doi:https://doi.org/10.1016/j.aei.2023.102105.
Çınar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
Cui, X., Lu, J. and Han, Y., 2023. Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability. Sensors, 24(1), p.165. https://doi.org/10.3390/s24010165
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Fashoto, S., Mbunge, E., Ogunleye, G., & Van den Burg, J. (2021). Implementation of Machine Learning for Predicting Maize Crop Yields using Multiple Linear Regression and Backward Elimination. Malaysian Journal of Computing, 6(1), p. 679. doi:https://doi.org/10.24191/mjoc.v6i1.8822.
García, C.B., García, J., López Martín, M.M., & Salmerón, R. (2014). Collinearity: revisiting the variance inflation factor in ridge regression. Journal of Applied Statistics, 42(3), pp. 648–661. doi:https://doi.org/10.1080/02664763.2014.980789.
Gardner-Frolick, R., Boyd, D., & Giang, A. (2022). Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework. Environmental Science & Technology, 56(5), pp. 2843–2860. doi:https://doi.org/10.1021/acs.est.1c01739.
Hayajneh, A.M., Alasali, F., Salama, A., & Holderbaum, W. (2024). Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions. IEEE Access, [online] 12, pp. 10846–10864. doi:https://doi.org/10.1109/ACCESS.2024.3354703.
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Inglis, A., Parnell, A., & Hurley, C.B. (2022). Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. Journal of Computational and Graphical Statistics, 31(3), pp.766–778. doi:https://doi.org/10.1080/10618600.2021.2007935.
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Kartal, E., & Altunkaynak, A. (2024). Empirical-Singular-Wavelet Based Machine Learning Models for Sea Level Forecasting in the Bosphorus Strait: A Performance Analysis. Ocean modelling (Oxford. Print), 188, pp. 102324–102324. doi:https://doi.org/10.1016/j.ocemod.2024.102324.
Karuppiah, K., Sankaranarayanan, B., & Ali, S.M. (2021). On sustainable predictive maintenance: Exploration of key barriers using an integrated approach. Sustainable Production and Consumption, 27, pp. 1537–1553. doi:https://doi.org/10.1016/j.spc.2021.03.023.
Kuhn, M., & Johnson, K. (2018). Max Kuhn and Kjell Johnson. Applied Predictive Modeling. New York, Springer. Biometrics, 74(1), pp. 383–383. doi:https://doi.org/10.1111/biom.12855.
Lee, S.M., Lee, D.H., & Kim, Y.S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, [online] 5(1). doi:https://doi.org/10.1186/s40887-019-0029-5.
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, [online] 104, pp. 799–834. doi:https://doi.org/10.1016/j.ymssp.2017.11.016.
Li, Z., Goebel, K., & Wu, D. (2018). Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning. Journal of Engineering for Gas Turbines and Power, 141(4), 041008. https://doi.org/10.1115/1.4041674
Li, P., Zhang, Z., Grosu, R., Deng, Z., Hou, J., Rong, Y., & Wu, R. (2022). An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries. Renewable and Sustainable Energy Reviews, 156, p.111843. doi:https://doi.org/10.1016/j.rser.2021.111843.
Liao, L., & Kottig, 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), pp. 191–207. doi:https://doi.org/10.1109/tr.2014.2299152.
Liao, L., & Köttig, F. (2016). A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Applied Soft Computing, 44, pp. 191–199. doi:https://doi.org/10.1016/j.asoc.2016.03.013.
Liu, Q., Liang, J., & Ma, O. (2020). A physics-based and data-driven hybrid modeling method for accurately simulating complex contact phenomenon. Multibody System Dynamics, 50(1), pp. 97–117. doi:https://doi.org/10.1007/s11044-020-09746-w.
Maulud, D., & Abdulazeez, A.M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), pp. 140–147. doi:https://doi.org/10.38094/jastt1457.
Melis, D.A., & Jaakkola, T. (2018). Towards Robust Interpretability with Self-Explaining Neural Networks. [online] Neural Information Processing Systems. Available at: https://proceedings.neurips.cc/paper/2018/hash/3e9f0fc9b2f89e043bc6233994dfcf76-Abstract.html [Accessed 9 Mar. 2024].
Mishra, D., Awasthi, U., Pattipati, K.R., & Bollas, G.M. (2023). Tool wear classification in precision machining using distance metrics and unsupervised machine learning. Journal of intelligent manufacturing. doi:https://doi.org/10.1007/s10845-023-02239-5.
Mobley, R.K. (2002). An Introduction to Predictive Maintenance. Elsevier.
Muneer, A., Taib, S.M., Naseer, S., Ali, R.F., & Aziz, I.A. (2021). Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis. Electronics, 10(20), p. 2453. doi:https://doi.org/10.3390/electronics10202453.
Natsumeda, M. (2023). e-RULENet: remaining useful life estimation with end-to-end learning from long run-to-failure data. SICE Journal of Control, Measurement, and System Integration, 16(1), pp. 164–171. doi:https://doi.org/10.1080/18824889.2023.2195982.
Raposo, F. (2016). Evaluation of analytical calibration based on least-squares linear regression for instrumental techniques: A tutorial review. TrAC Trends in Analytical Chemistry, 77, pp. 167–185. doi:https://doi.org/10.1016/j.trac.2015.12.006.
Rial, D., Kebir, H., Wintrebert, E., & Roelandt, J.-M. (2014). Multiaxial fatigue analysis of a metal flexible pipe. Materials & Design (1980-2015), 54, pp. 796–804. doi:https://doi.org/10.1016/j.matdes.2013.08.105.
Rong, M., Gong, D., & Gao, X. (2019). Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends. IEEE Access, 7, pp. 19709–19725. doi:https://doi.org/10.1109/access.2019.2894366.
Salahuddin, Z., Woodruff, H.C., Chatterjee, A., & Lambin, P. (2022). Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in Biology and Medicine, [online] 140, p. 105111. doi:https://doi.org/10.1016/j.compbiomed.2021.105111.
Saxena, A., and Goebel, K. (2008). PHM08 Challenge Data Set, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. Available at: https://data.nasa.gov/download/nk8v-ckry/application%2Fzip
Shachar, N., Mitelpunkt, A., Kozlovski, T., Galili, T., Frostig, T., Brill, B., Marcus-Kalish, M., & Benjamini, Y. (2018). The Importance of Nonlinear Transformations Use in Medical Data Analysis. JMIR Medical Informatics, 6(2), p. e27. doi:https://doi.org/10.2196/medinform.7992.
Shaheen, B., Kocsis, Á., & Németh, I. (2023). Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks. Engineering Applications of Artificial Intelligence, 119, p. 105749. doi:https://doi.org/10.1016/j.engappai.2022.105749.
Shim, H., Bonifay, W., & Wiedermann, W. (2022). Parsimonious asymmetric item response theory modeling with the complementary log-log link. Behavior Research Methods. doi:https://doi.org/10.3758/s13428-022-01824-5.
Talaat, F.M., Aljadani, A., Alharthi, B., Farsi, M., Badawy, M., & Elhosseini, M.A. (2023). A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing. Mathematics, 11(18), pp. 3930–3930. doi:https://doi.org/10.3390/math11183930.
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, p. 107864. doi:https://doi.org/10.1016/j.ress.2021.107864.
Victoria, M., & E. Priyadarshini (2023). Aeroengine gas trajectory prediction using time-series analysis auto-regressive integrated moving average. Aircraft Engineering and Aerospace Technology. doi:https://doi.org/10.1108/aeat-01-2023-0018.
Xiong, M., & Wang, H. (2022). Digital twin applications in aviation industry: A review. The International Journal of Advanced Manufacturing Technology, 121. doi:https://doi.org/10.1007/s00170-022-09717-9.
Yu, H., & Hutson, A.D. (2022). Inferential procedures based on the weighted Pearson correlation coefficient test statistic. Journal of Applied Statistics, pp. 1–16. doi:https://doi.org/10.1080/02664763.2022.2137477.
Zhang, W., Yang, D., & Wang, H. (2019). Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Systems Journal, [online] 13(3), pp. 2213–2227. doi:https://doi.org/10.1109/JSYST.2019.2905565.
Zhang, Y., Xin, Y., Li, Q., Ma, J., Li, S., Lv, X., & Lv, W. (2017). Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications. BioMedical Engineering OnLine, 16(1). doi:https://doi.org/10.1186/s12938-017-0416-x.
Zhao, J., Han, X., Ouyang, M., & Burke, A.F. (2023). Specialized deep neural networks for battery health prognostics: Opportunities and challenges. Journal of Energy Chemistry, [online] 87, pp.416–438. doi:https://doi.org/10.1016/j.jechem.2023.08.047.
Angelov, P., Filev, D.P., & Kasabov, N. (2010). Evolving Intelligent Systems. John Wiley & Sons.
Babu, G.S., Zhao, P., & Li, X.-L. (2016). Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life. Database Systems for Advanced Applications, 9642, pp. 214–228. doi:https://doi.org/10.1007/978-3-319-32025-0_14.
Cheng, J., Sun, J., Yao, K., Xu, M., & Cao, Y. (2022). A variable selection method based on mutual information and variance inflation factor. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 268, p. 120652. doi:https://doi.org/10.1016/j.saa.2021.120652.
Chinta, V.S., Kethi Reddi, S., & Yarramsetty, N. (2023). Optimal feature selection on Serial Cascaded deep learning for predictive maintenance system in automotive industry with fused optimization algorithm. Advanced Engineering Informatics, [online] 57, p. 102105. doi:https://doi.org/10.1016/j.aei.2023.102105.
Çınar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
Cui, X., Lu, J. and Han, Y., 2023. Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability. Sensors, 24(1), p.165. https://doi.org/10.3390/s24010165
Darlington, R.B., & Hayes, A.F. (2017). Regression analysis and linear models: concepts, applications, and implementation. New York: The Guilford Press.
Fashoto, S., Mbunge, E., Ogunleye, G., & Van den Burg, J. (2021). Implementation of Machine Learning for Predicting Maize Crop Yields using Multiple Linear Regression and Backward Elimination. Malaysian Journal of Computing, 6(1), p. 679. doi:https://doi.org/10.24191/mjoc.v6i1.8822.
García, C.B., García, J., López Martín, M.M., & Salmerón, R. (2014). Collinearity: revisiting the variance inflation factor in ridge regression. Journal of Applied Statistics, 42(3), pp. 648–661. doi:https://doi.org/10.1080/02664763.2014.980789.
Gardner-Frolick, R., Boyd, D., & Giang, A. (2022). Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework. Environmental Science & Technology, 56(5), pp. 2843–2860. doi:https://doi.org/10.1021/acs.est.1c01739.
Hayajneh, A.M., Alasali, F., Salama, A., & Holderbaum, W. (2024). Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions. IEEE Access, [online] 12, pp. 10846–10864. doi:https://doi.org/10.1109/ACCESS.2024.3354703.
Hong, C.W., Lee, C., Lee, K., Ko, M.-S., Kim, D.E., & Hur, K. (2020). Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction. Sensors, 20(22), p. 6626. doi:https://doi.org/10.3390/s20226626.
Hu, Y., Liu, S., Lu, H., & Zhang, H. (2019). Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method. Chinese Journal of Mechanical Engineering, 32(1). doi:https://doi.org/10.1186/s10033-019-0317-y.
Inglis, A., Parnell, A., & Hurley, C.B. (2022). Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. Journal of Computational and Graphical Statistics, 31(3), pp.766–778. doi:https://doi.org/10.1080/10618600.2021.2007935.
Javaid, M., Haleem, A., Singh, R.P., Rab, S., & Suman, R. (2021). Significance of Sensors for Industry 4.0: Roles, Capabilities, and Applications. Sensors International, [online] 2, p. 100110. doi:https://doi.org/10.1016/j.sintl.2021.100110.
Kartal, E., & Altunkaynak, A. (2024). Empirical-Singular-Wavelet Based Machine Learning Models for Sea Level Forecasting in the Bosphorus Strait: A Performance Analysis. Ocean modelling (Oxford. Print), 188, pp. 102324–102324. doi:https://doi.org/10.1016/j.ocemod.2024.102324.
Karuppiah, K., Sankaranarayanan, B., & Ali, S.M. (2021). On sustainable predictive maintenance: Exploration of key barriers using an integrated approach. Sustainable Production and Consumption, 27, pp. 1537–1553. doi:https://doi.org/10.1016/j.spc.2021.03.023.
Kuhn, M., & Johnson, K. (2018). Max Kuhn and Kjell Johnson. Applied Predictive Modeling. New York, Springer. Biometrics, 74(1), pp. 383–383. doi:https://doi.org/10.1111/biom.12855.
Lee, S.M., Lee, D.H., & Kim, Y.S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, [online] 5(1). doi:https://doi.org/10.1186/s40887-019-0029-5.
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, [online] 104, pp. 799–834. doi:https://doi.org/10.1016/j.ymssp.2017.11.016.
Li, Z., Goebel, K., & Wu, D. (2018). Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning. Journal of Engineering for Gas Turbines and Power, 141(4), 041008. https://doi.org/10.1115/1.4041674
Li, P., Zhang, Z., Grosu, R., Deng, Z., Hou, J., Rong, Y., & Wu, R. (2022). An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries. Renewable and Sustainable Energy Reviews, 156, p.111843. doi:https://doi.org/10.1016/j.rser.2021.111843.
Liao, L., & Kottig, 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), pp. 191–207. doi:https://doi.org/10.1109/tr.2014.2299152.
Liao, L., & Köttig, F. (2016). A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Applied Soft Computing, 44, pp. 191–199. doi:https://doi.org/10.1016/j.asoc.2016.03.013.
Liu, Q., Liang, J., & Ma, O. (2020). A physics-based and data-driven hybrid modeling method for accurately simulating complex contact phenomenon. Multibody System Dynamics, 50(1), pp. 97–117. doi:https://doi.org/10.1007/s11044-020-09746-w.
Maulud, D., & Abdulazeez, A.M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), pp. 140–147. doi:https://doi.org/10.38094/jastt1457.
Melis, D.A., & Jaakkola, T. (2018). Towards Robust Interpretability with Self-Explaining Neural Networks. [online] Neural Information Processing Systems. Available at: https://proceedings.neurips.cc/paper/2018/hash/3e9f0fc9b2f89e043bc6233994dfcf76-Abstract.html [Accessed 9 Mar. 2024].
Mishra, D., Awasthi, U., Pattipati, K.R., & Bollas, G.M. (2023). Tool wear classification in precision machining using distance metrics and unsupervised machine learning. Journal of intelligent manufacturing. doi:https://doi.org/10.1007/s10845-023-02239-5.
Mobley, R.K. (2002). An Introduction to Predictive Maintenance. Elsevier.
Muneer, A., Taib, S.M., Naseer, S., Ali, R.F., & Aziz, I.A. (2021). Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis. Electronics, 10(20), p. 2453. doi:https://doi.org/10.3390/electronics10202453.
Natsumeda, M. (2023). e-RULENet: remaining useful life estimation with end-to-end learning from long run-to-failure data. SICE Journal of Control, Measurement, and System Integration, 16(1), pp. 164–171. doi:https://doi.org/10.1080/18824889.2023.2195982.
Raposo, F. (2016). Evaluation of analytical calibration based on least-squares linear regression for instrumental techniques: A tutorial review. TrAC Trends in Analytical Chemistry, 77, pp. 167–185. doi:https://doi.org/10.1016/j.trac.2015.12.006.
Rial, D., Kebir, H., Wintrebert, E., & Roelandt, J.-M. (2014). Multiaxial fatigue analysis of a metal flexible pipe. Materials & Design (1980-2015), 54, pp. 796–804. doi:https://doi.org/10.1016/j.matdes.2013.08.105.
Rong, M., Gong, D., & Gao, X. (2019). Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends. IEEE Access, 7, pp. 19709–19725. doi:https://doi.org/10.1109/access.2019.2894366.
Salahuddin, Z., Woodruff, H.C., Chatterjee, A., & Lambin, P. (2022). Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in Biology and Medicine, [online] 140, p. 105111. doi:https://doi.org/10.1016/j.compbiomed.2021.105111.
Saxena, A., and Goebel, K. (2008). PHM08 Challenge Data Set, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. Available at: https://data.nasa.gov/download/nk8v-ckry/application%2Fzip
Shachar, N., Mitelpunkt, A., Kozlovski, T., Galili, T., Frostig, T., Brill, B., Marcus-Kalish, M., & Benjamini, Y. (2018). The Importance of Nonlinear Transformations Use in Medical Data Analysis. JMIR Medical Informatics, 6(2), p. e27. doi:https://doi.org/10.2196/medinform.7992.
Shaheen, B., Kocsis, Á., & Németh, I. (2023). Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks. Engineering Applications of Artificial Intelligence, 119, p. 105749. doi:https://doi.org/10.1016/j.engappai.2022.105749.
Shim, H., Bonifay, W., & Wiedermann, W. (2022). Parsimonious asymmetric item response theory modeling with the complementary log-log link. Behavior Research Methods. doi:https://doi.org/10.3758/s13428-022-01824-5.
Talaat, F.M., Aljadani, A., Alharthi, B., Farsi, M., Badawy, M., & Elhosseini, M.A. (2023). A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing. Mathematics, 11(18), pp. 3930–3930. doi:https://doi.org/10.3390/math11183930.
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, p. 107864. doi:https://doi.org/10.1016/j.ress.2021.107864.
Victoria, M., & E. Priyadarshini (2023). Aeroengine gas trajectory prediction using time-series analysis auto-regressive integrated moving average. Aircraft Engineering and Aerospace Technology. doi:https://doi.org/10.1108/aeat-01-2023-0018.
Xiong, M., & Wang, H. (2022). Digital twin applications in aviation industry: A review. The International Journal of Advanced Manufacturing Technology, 121. doi:https://doi.org/10.1007/s00170-022-09717-9.
Yu, H., & Hutson, A.D. (2022). Inferential procedures based on the weighted Pearson correlation coefficient test statistic. Journal of Applied Statistics, pp. 1–16. doi:https://doi.org/10.1080/02664763.2022.2137477.
Zhang, W., Yang, D., & Wang, H. (2019). Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Systems Journal, [online] 13(3), pp. 2213–2227. doi:https://doi.org/10.1109/JSYST.2019.2905565.
Zhang, Y., Xin, Y., Li, Q., Ma, J., Li, S., Lv, X., & Lv, W. (2017). Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications. BioMedical Engineering OnLine, 16(1). doi:https://doi.org/10.1186/s12938-017-0416-x.
Zhao, J., Han, X., Ouyang, M., & Burke, A.F. (2023). Specialized deep neural networks for battery health prognostics: Opportunities and challenges. Journal of Energy Chemistry, [online] 87, pp.416–438. doi:https://doi.org/10.1016/j.jechem.2023.08.047.
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