Abstract
As a complex and expensive system, the aero-engine is confronted with many problems that are difficult to be handled by traditional methods, especially in the remaining useful life (RUL) estimation field. In recent studies, the accuracy and stability of remaining useful life prediction are still defective. In this paper, a new paralleled semi-supervised network is proposed to predict the remaining useful life of the turbofan engine to improve the stability of the predicted results. To demonstrate the effectiveness of the system, experimental verification is carried out by using the popular commercial modular aero propulsion system simulation (C-MAPSS) data set which is published by the national aeronautics and space administration (NASA). Moreover, the superiority of this method is proved by comparing with the other semi-supervised learning methods. The results of this study suggest that this paralleled semi-supervised training model is a new and promising approach.
Supported by the National Natural Science Foundation of China under Grant Nos 61890920 & 61890921 and LiaoNing Revitalization Talents Program XLYC1808015.
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Wang, T., Guo, D., Sun, X. (2021). A New Paralleled Semi-supervised Deep Learning Method for Remaining Useful Life Prediction. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_20
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DOI: https://doi.org/10.1007/978-981-16-2336-3_20
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