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Research on the prediction algorithm of aero engine lubricating oil consumption based on multi-feature information fusion

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Abstract

The lubrication system supplies lubrication and cleans the rotating parts and contacting machinery during the operation of an aero-engine. It is crucial to maintain an adequate amount of lubricant by predicting and analyzing the consumption rate to ensure endurance and maintenance programs are effective. This paper examines the combination of temporal and non-temporal data that impact the characteristic parameters of lubricant consumption rate in aero-engines. Our study focuses on the merging of LSTM (Long Short-Term Memory) + LightGBM (Light Gradient Boosting Machine) + CatBoost, and uses KPCA dimensionality reduction optimization, along with Stacking for the fusion of a multi-feature regression prediction algorithm. On the one hand, this study utilizes integrated learning to fuse feature extractions from LSTM for temporal information and non-temporal information by GDBT (Gradient Boosting Decision Tree). This approach considers the trend and distribution of feature samples to develop a more robust feature extraction method. On the other hand, the integrated learning framework incorporates multi-decision making and feature importance extraction to strengthen the mapping relationship with the predicted output of lubrication oil consumption rate, enabling regression prediction. The algorithm for regression prediction has been executed and the results indicate a final regression prediction MAPE (Mean Absolute Percentage Error) of less than 3%. MSE and RMSE reached 1.28% and 1.33%, the results are in an ideal state. The algorithms used in this paper will be applied in the future to aero-engine lubricant systems and eventually to engines in general.

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Data availability

The data that support the findings of this study are available from [China Gas Turbine Establishment] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [China Gas Turbine Establishment].

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Authors and Affiliations

  1. School of Power and Energy, Northwestern Polytechnical University, Xian, China

    Qifan Zhou, Yingqing Guo, Kejie Xu, Guicai Li & Yunhui Dong

  2. School of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Bosong Chai

  3. Institute for Aero Engine, Tsinghua University, Beijing, China

    Kun Wang

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  1. Qifan Zhou
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  2. Yingqing Guo
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Correspondence to Qifan Zhou.

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Zhou, Q., Guo, Y., Xu, K. et al. Research on the prediction algorithm of aero engine lubricating oil consumption based on multi-feature information fusion. Appl Intell 54, 11845–11875 (2024). https://doi.org/10.1007/s10489-024-05759-6

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