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Aero-engine remaining useful life prediction based on a long-term channel self-attention network

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Abstract

The accurate prediction of remaining useful life (RUL) is conducive to reducing equipment failure rates and maintenance costs. As the long-term operation of equipments under the normal conditions, the proportion of fault data in the total monitoring data is relatively small. To fully extract the effective degradation features from these fault data, a novel model called long-term channel self-attention network (LTCSAN) is proposed for accurate RUL prediction in this paper. Firstly, a data-level evaluation (DLE) network is connected in parallel with CNNs to focus on the local degradation features of the device, where the receptive field is progressively getting larger. On this basis, a long-term channel attention network (LTCANet) is constructed to further interact the information among sensors and cycles for capturing effective long-term dependencies. Finally, the features with valuable degradation trend information are fed into the fully connected (FC) layer for RUL prediction. To validate the efficacy of LTCSAN, experiments are carried out on the public dataset of commercial modular aero-propulsion system simulation (C-MAPSS). The average RMSE and score of the proposed method are 14.03 and 713, respectively, under different working conditions. The comparison results with other state-of-the-art methods demonstrate the superiority of LTCSAN.

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Availability of data and materials

Some of the data and materials supporting the results of this study can be found in public dataset [27], and others can be obtained from the corresponding authors upon reasonable request.

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Funding

This work was partially supported by the National Natural Science Foundation of China for Excellent Young Scholars under Grant No. 62322315, the National Natural Science Foundation of China under Grant No. 61873237, the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars under Grant No. LR22F030003, the National Key R &D Funding under Grant No. 2018YFB1403702, the Key R &D Programs of Zhejiang Province under Grant NO. 2023C01224, and Major Project of Science and Technology Innovation in Ningbo City under Grant No. 2019B1003.

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Authors and Affiliations

  1. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang Province, China

    Xuezhen Liu, Yongyi Chen, Hongjie Ni & Dan Zhang

  2. Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, 21381, Saudi Arabia

    Dan Zhang

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  1. Xuezhen Liu
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  2. Yongyi Chen
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  4. Dan Zhang
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Contributions

XL and YC contributed to conceptualization, methodology, data processing, experimental validation and manuscript writing. HN and DZ contributed to the modification of the manuscript.

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Correspondence to Xuezhen Liu.

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Liu, X., Chen, Y., Ni, H. et al. Aero-engine remaining useful life prediction based on a long-term channel self-attention network. SIViP 18, 637–645 (2024). https://doi.org/10.1007/s11760-023-02800-y

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