Abstract
Aiming at the problems of low accuracy and long calculation time of remaining useful life (RUL) prediction of aero-engine by the data-driven method, a method for mining aero-engine information in tensor mode is proposed in this paper. Different from the traditional vector mode of data feature extraction, the tensor form of data can reflect the structure and correlation information among data. It has certain advantages when dealing with data that has strong coupling such as aero-engine signals. A large turbofan engine degradation simulation dataset (C-MAPSS dataset) with strong coupling provided by NASA is used to validate the method proposed in this paper. The results show that the data extracted by tensor decomposition method has better training effect than the unprocessed data in convolutional neural network (CNN), with the average prediction accuracy improved by 28.39%, and the average learning time shortened by 24.25%.
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References
Jaw, L.C., Mattingly, J.D.: Aircraft Engine Controls: Design, System Analysis, and Health Monitoring, pp. 136–138 (2009)
Vachtsevanos, G.J., Vachtsevanos, G.J.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley Online Library (2006)
Li, X., Ding, Q., Sun, J.-Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Saf. 172, 1–11 (2018)
Zhao, H., Zheng, N., Chen, T., Wei, K.: Aero engine rul prediction based on the combination of similarity and PSO-SVR (2021)
Li, H., Li, Y., Wang, Z., Li, Z.: Remaining useful life prediction of aero-engine based on PCA-LSTM. In: 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO), pp. 63–66. IEEE (2021)
Wang, T., Guo, D., Sun, X.-M.: Remaining useful life predictions for turbofan engine degradation based on concurrent semi-supervised model. Neural Comput. Appl. 34(7), 5151–5160 (2021). https://doi.org/10.1007/s00521-021-06089-1
Wu, B., Shi, H., Zeng, J., Shi, G., Qin, Y.: Multi-sensor information fusion-based remaining useful life prediction with nonlinear wiener process. Meas. Sci. Technol. 33, 105106 (2022)
Yuan, M., Wu, Y., Lin, L.: Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In: IEEE International Conference on Aircraft Utility Systems (AUS), pp. 135–140. IEEE (2016)
Zhang, X., et al.: Remaining useful life estimation using CNN-XGB with extended time window. IEEE Access 7, 154386–154397 (2019)
Al-Dulaimi, A., Zabihi, S., Asif, A., Mohammadi, A.: Hybrid deep neural network model for remaining useful life estimation. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3872–3876. IEEE (2019)
Ge, Y., Zhang, F.: Remaining useful life estimation for aero-engine with multiple working conditions via an improved generative adversarial network. J. Braz. Soc. Mech. Sci. Eng. 44(5), 1–12 (2022)
Jing, C., Li, Z., Ping, D.: Remaining useful life prediction for Aero-Engines combining sate space model and KF algorithm. Trans. Nanjing Univ. Aeronaut. Astronaut. 34(03), 265–271 (2017)
da Costa, P.R.d.O., Akçay, A., Zhang, Y., Kaymak, U.: Remaining useful lifetime prediction via deep domain adaptation. Reliab. Eng. Syst. Saf. 195, 106682 (2020)
Xianda, Z.: Matrix Analysis and Application. Tsinghua University Press Co., Ltd., Beijing (2004)
Cichocki, A., et al.: Tensor Decompositions for Signal Processing Applications: from two-way to multiway component analysis. IEEE Signal Process. Mag. 32(2), 145–163 (2015)
Hou, C., Nie, F., Zhang, C., Yi, D., Wu, Y.: Multiple rank multi-linear SVM for matrix data classification. Pattern Recogn. 47(1), 454–469 (2014)
Cammoun, L., et al.: A review of tensors and tensor signal processing. In: Aja-Fernández, S., de Luis García, R., Tao, D., Li, X. (eds.) Tensors in Image Processing and Computer Vision, pp. 1–32. Springer, London (2009). https://doi.org/10.1007/978-1-84882-299-3_1
Sidiropoulos, N.D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E.E., Faloutsos, C.: Tensor decomposition for signal processing and machine learning. IEEE Trans. Signal Process. 65(13), 3551–3582 (2017)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International Conference on Prognostics and Health Management, pp. 1–9. IEEE (2008)
Che, M., Wei, Y.: Randomized algorithms for the approximations of Tucker and the tensor train decompositions. Adv. Comput. Math. 45(1), 395–428 (2018). https://doi.org/10.1007/s10444-018-9622-8
Zeng, C., Ng, M.K.: Decompositions of third-order tensors: HOSVD, T-SVD, and Beyond. Numer. Linear Algebra Appl. 27(3), e2290 (2020)
Sheehan, B.N., Saad, Y.: Higher order orthogonal iteration of tensors (HOOI) and its relation to PCA and GLRAM. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 355–365. SIAM (2007)
Sun, J., Tao, D., Papadimitriou, S., Yu, P.S., Faloutsos, C.: Incremental tensor analysis: Theory and applications. ACM Trans. Knowl. Disc. Data (TKDD) 2(3), 1–37 (2008)
Zhong, M., Jiansheng, G., Taoyong, G., Sheng, M.: Remaining useful life prediction of aero-engine based on improved convolutional neural network. J. Air Force Eng. Univ. (Nat. Sci. Ed.) 21(06), 19–25 (2020)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)
Wu, J.: Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University, China, vol. 5, no. 23, p. 495 (2017)
Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. (2021)
Jogin, M., Madhulika, M., Divya, G., Meghana, R., Apoorva, S.: Feature extraction using convolution neural networks (CNN) and deep learning. In: 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), pp. 2319–2323. IEEE (2018)
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Jiang, J., Wang, X., Hu, Z. (2022). Aero-Engine Remaining Useful Life Prediction via Tensor Decomposition Method. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_42
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