Fault Prognosis of Turbofan Engines Eventual Failure Prediction and Remaining Useful Life Estimation



Published Aug 8, 2023
Joseph Cohen Xun Huan Jun Ni


In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN–Flux with PCA augmentation, achieves AUROC and AUPR scores exceeding 0.94 for each classification on average. In addition to predicting eventual failures with high accuracy, ANN–Flux achieves comparable remaining useful life RMSE for the same test split of the dataset when benchmarked against past work, with significantly less computational cost.

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Prognostics and health management, supervised machine learning, principal components analysis

Bagavathiappan, S., Lahiri, B. B., Saravanan, T., Philip, J., & Jayakumar, T. (2013). Infrared thermography for condition monitoring – A review. Infrared Physics & Technology, 60, 35–55. doi: 10.1016/J.INFRARED.2013.03.006
Berghout, T., Mouss, M. D., Mouss, L. H., & Benbouzid, M. (2022, 12). ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions. Aerospace 2023, 10, 10. doi: 10.3390/AEROSPACE10010010
Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2014). Julia: A Fresh Approach to Numerical Computing. SIAM Review, 59, 65–98.
Biggio, L., Wieland, A., Chao, M. A., Kastanis, I., & Fink, O. (2021). Uncertainty-Aware Prognosis via Deep Gaussian Process. IEEE Access, 9, 123527. doi: 10.1109/ACCESS.2021.3110049
Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory Optimization. Procedia CIRP, 59, 184–189. doi: 10.1016/J.PROCIR.2016.09.015
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2021a). Aircraft Engine Run-To-Failure Dataset Under Real Flight Conditions. NASA Ames Prognostics Data Repository.
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2021b). PHM Society Data Challenge 2021. PHM Society, 1-6.
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2022, 1). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217, 107961. doi: 10.1016/J.RESS.2021.107961
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In (pp. 785–794). ACM. doi: 10.1145/2939672
Coble, J., Ramuhalli, P., Bond, L., Hines, J. W., & Upadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power plants. International Journal of Prognostics and Health Management, 6(3), 1–22.
DeVol, N., Saldana, C., & Fu, K. (2021). Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines. In (Vol. 13). PHM Society. doi: 10.36001/PHMCONF.2021.v13i1.3109
Genuer, R., Poggi, J. M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017). Random Forests for Big Data. Big Data Research, 9, 28–46. doi: 10.1016/j.bdr.2017.07.003
Gupta, M., Wadhvani, R., & Rasool, A. (2023, 1). A realtime adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowledge-Based Systems, 259, 110070. doi: 10.1016/J.KNOSYS.2022.110070
Innes, M. (2018). Flux: Elegant machine learning with Julia. Journal of Open Source Software, 3. doi: 10.21105/JOSS.00602
Innes, M., Edelman, A., Fischer, K., Rackauckas, C., Saba, E., Shah, V. B., & Tebbutt,W. (2019). A Differentiable Programming System to Bridge Machine Learning and Scientific Computing.
Jollife, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci, 374(2065). doi: 10.1098/RSTA.2015.0202
Kong, Z., Jin, X., Xu, Z., & Zhang, B. (2022). Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network. IEEE Transactions on Instrumentation and Measurement, 71. doi: 10.1109/TIM.2022.3184352
Lai, X., Qiu, T., Shui, H., Ding, D., & Ni, J. (2023, 4). Predicting future production system bottlenecks with a graph neural network approach. Journal of Manufacturing Systems, 67, 201-212. doi: 10.1016/J.JMSY.2023.01.010
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), 314–334. doi: 10.1016/j.ymssp.2013.06.004
Li, C., Sanchez, R. V., Zurita, G., Cerrada, M., Cabrera, D., & Vásquez, R. E. (2016). Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing, 76–77, 283–293. doi: 10.1016/J.YMSSP.2016.02.007
Li, T., Zhou, Z., Li, S., Sun, C., Yan, R., & Chen, X. (2022, 4). The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study. Mechanical Systems and Signal Processing, 168, 108653. doi: 10.1016/J.YMSSP.2021.108653
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, 191–207. doi: 10.1109/TR.2014.2299152
Lövberg, A. (2021, 12). Remaining Useful Life Prediction of Aircraft Engines with Variable Length Input Sequences. In (Vol. 13). PHM Society. doi: 10.36001/PHMCONF.2021.V13I1.3108
Mahamad, A. K., Saon, S., & Hiyama, T. (2010). Predicting remaining useful life of rotating machinery based artificial neural network. Computers and Mathematics with Applications, 60(4), 1078–1087. doi: 10.1016/J.CAMWA.2010.03.065
Maier, O., Wilms, M., von der Gablentz, J., Krämer, U. M., Münte, T. F., & Handels, H. (2015). Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. Journal of Neuroscience Methods, 240, 89–100. doi: 10.1016/j.jneumeth.2014.11.011
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Édouard Duchesnay (2012). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Santos, P., Maudes, J., & Bustillo, A. (2018). Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. Journal of Intelligent Manufacturing, 29(2), 333–351. doi: 10.1007/S10845-015-1110-0
Senoner, J., Netland, T., & Feuerriegel, S. (2022). Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing. Management Science, 68(8), 5704-5723. doi: 10.1287/mnsc.2021.4190
Shao, H., Jiang, H., Lin, Y., & Li, X. (2018). A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing, 102, 278–297. doi: 10.1016/J.YMSSP.2017.09.026
Solís-Martín, D., Galán-Páez, J., & Borrego-Díaz, J. (2021). A Stacked Deep Convolutional Neural Network to Predict the Remaining Useful Life of a Turbofan Engine. In (Vol. 13). PHM Society. doi: 10.36001/PHMCONF.2021.V13I1.3110
Song, T., Liu, C.,Wu, R., Jin, Y., & Jiang, D. (2022, 5). A hierarchical scheme for remaining useful life prediction with long short-term memory networks. Neurocomputing, 487, 22-33. doi: 10.1016/J.NEUCOM.2022.02.032
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015, 793161. doi: 10.1155/2015/793161
Wu, J. Y., Wu, M., Chen, Z., Li, X., & Yan, R. (2021, 1). A joint classification-regression method for multi-stage remaining useful life prediction. Journal of Manufacturing Systems, 58, 109-119. doi: 10.1016/J.JMSY.2020.11.016
Wu, X., & Ye, Q. (2016, 7). Fault diagnosis and prognostic of solid oxide fuel cells. Journal of Power Sources, 321, 47-56. doi: 10.1016/J.JPOWSOUR.2016.04.080
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