RUL Estimation Enhancement using Hybrid Deep Learning Methods
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Published
May 3, 2021
Ikram Remadna
Labib Sadek Terrissa
Soheyb Ayad
Nourddine Zerhouni
Abstract
The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers.
In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature.
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Keywords
PHM, Hybrid deep Learning, CNN, BDGRU, BDLSTM, Remaining Useful Life, RUL estimation
References
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Yuan, M., Wu, Y., & Lin, L. (2016). Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network. In 2016 ieee international conference on aircraft utility systems (aus) (pp. 135–140).
Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction. Journal of manufacturing systems, 48, 78–86.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. In 2017 ieee international conference on prognostics and health management (icphm) (pp. 88–95).
Babu, G. S., 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).
Belaala, A., Bourezane, Y., Terrissa, L. S., Al Masry, Z., & Zerhouni, N. (2020). Skin cancer and deep learning for dermoscopic images classification: A pilot study. American Society of Clinical Oncology.
Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019). Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering & System Safety, 185, 372–382.
Collobert, R., & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on machine learning (pp. 160–167).
Demšar, U., Harris, P., Brunsdon, C., Fotheringham, A. S., & McLoone, S. (2013). Principal component analysis on spatial data: an overview. Annals of the Association of American Geographers, 103(1), 106–128.
Ding, S.-H., & Kamaruddin, S. (2015). Maintenance policy optimization—literature review and directions. The International Journal of Advanced Manufacturing Technology, 76(5-8), 1263–1283.
Gouriveau, R., Medjaher, K., Ramasso, E., & Zerhouni, N. (2013). Phm-prognostics and health management-de la surveillance au pronostic de défaillances de syst`emes complexes. Techniques de l’Ingénieur, 9.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2017). Du concept de phm `a la maintenance prédictive 1: Surveillance et pronostic (Vol. 3). ISTE Group.
Guo, C. (2015). Study on the recognition of aero-engine blade-casing rubbing fault based on the casing vibration acceleration. Measurement, 65, 71–80.
He, K., & Sun, J. (2015). Convolutional neural networks at constrained time cost. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 5353–5360).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Hsu, C.-S., & Jiang, J.-R. (2018). Remaining useful life estimation using long short-term memory deep learning. In 2018 ieee international conference on applied system invention (icasi) (pp. 58–61).
Javed, K., Gouriveau, R., & Zerhouni, N. (2017). State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels. Mechanical Systems and Signal Processing, 94, 214–236.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1–11.
Liao, Y., Zhang, L., & Liu, C. (2018). Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method. In 2018 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Ma, J., Su, H., Zhao, W.-l., & Liu, B. (2018). Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer selflearning. Complexity, 2018.
Mierswa, I., & Morik, K. (2005). Automatic feature extraction for classifying audio data. Machine learning, 58(2-3), 127–149.
Patro, S., & Sahu, K. K. (2015). Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462.
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500), 2323–2326.
Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Sharma, A., & Paliwal, K. K. (2015). Linear discriminant analysis for the small sample size problem: an overview. International Journal of Machine Learning and Cybernetics, 6(3), 443–454.
Song, Y., Shi, G., Chen, L., Huang, X., & Xia, T. (2018). Remaining useful life prediction of turbofan engine using hybrid model based on autoencoder and bidirectional long short-term memory. Journal of Shanghai Jiaotong University (Science), 23(1), 85–94.
Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. science, 290(5500), 2319–2323.
Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 2018.
Wang, J., Wen, G., Yang, S., & Liu, Y. (2018). Remaining useful life estimation in prognostics using deep bidirectional lstm neural network. In 2018 prognostics and system health management conference (phmchongqing) (pp. 1037–1042).
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.
Yuan, M., Wu, Y., & Lin, L. (2016). Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network. In 2016 ieee international conference on aircraft utility systems (aus) (pp. 135–140).
Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction. Journal of manufacturing systems, 48, 78–86.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. In 2017 ieee international conference on prognostics and health management (icphm) (pp. 88–95).
Section
Technical Papers