Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines
Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. This work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.
remaining useful life, turbofan engines, convolutional neural networks
Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv.
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, 1–6. doi: 10.1109/ICEngTechnol.2017.8308186
Bolander, N., Qiu, H., Eklund, N., Hindle, E., & Rosenfeld, T. (2009). Physics-based remaining useful life prediction for aircraft engine bearing prognosis. Annual Conference of the PHM Society, 1(1), 1–10.
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-datarepository), NASA Ames Research Center, Moffett Field, CA. doi: 10.3390/data6010005
Chollet, F., et al. (2015). Keras. https://keras.io.
Coble, J., & Hines, J. W. (2011). Applying the general path model to estimation of remaining useful life. International Journal of Prognostics and Health Management, 2(1), 71–82.
da Costa, P. R. d. O., Akeay, A., Zhang, Y., & Kaymak, U. (2019). Attention and Long Short-Term Memory Network for Remaining Useful Lifetime Predictions of Turbofan Engine Degradation. International Journal of PHM Society, 10(4), 1–12. doi: 10.1115/GTINDIA2019-2368
DeVol, N., Saldana, C., Fu, K., & Woodruff, G. W. (2021). Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines, 1.
Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7), 1895–1923.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (cvpr).
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi: 10.1016/j.ymssp.2005.09.012
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv. doi: 10.48550/ARXIV.1412.6980
Kong, H. B., Jo, S. H., Jung, J. H., Ha, J. M., Shin, Y. C., Yoon, H., . . . Jeon, B. C. (2020). A hybrid approach of data-driven and physics-based methods for estimation and prediction of fatigue crack growth. International Journal of Prognostics and Health Management, 11, 1–12. doi: 10.36001/ijphm.2020.v11i1.2605
Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering and System Safety, 172, 1–11. doi: 10.1016/j.ress.2017.11.021
Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv. doi: 10.48550/ARXIV.1609.04747
Sateesh Babu, G., Zhao, P., & Li, X.-L. (2016). Deep convolutional neural network based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214–228).
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. doi: 10.1109/PHM.2008.4711414
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. doi: 10.1016/j.ejor.2010.11.018
Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836. doi: 10.1016/j.ymssp.2010.11.018
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929–1958.
Szegeandy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 1–9. doi: 10.1109/CVPR.2015.7298594
Wu, J., Hu, K., Cheng, Y., Zhu, H., Shao, X., & Wang, Y. (2020). Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Transactions, 97, 241–250. doi: 10.1016/j.isatra.2019.07.004
Yang, H., Zhao, F., Jiang, G., Sun, Z., & Mei, X. (2019). A novel deep learning approach for machinery prognostics based on time windows. Applied Sciences, 9(22), 4813. doi: 10.3390/app9224813
Yang, L., & Shami, A. (2020). On hyper-parameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316. doi: https://doi.org/10.1016/j.neucom.2020.07.061
Zhao, C., Huang, X., Li, Y., & Iqbal, M. Y. (2020). A double-channel hybrid deep neural network based on CNN and BiLSTM for remaining useful life prediction. Sensors (Switzerland), 20(24), 1–15. doi: 10.3390/s20247109
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long Short-Term Memory Network for Remaining Useful Life estimation. 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017, 88–95. doi: 10.1109/ICPHM.2017.7998311