Embedded implementation method of complex equipment health state prediction based on LightGBM

Sen Wang; Wei Niu; Danning Zhang; Tanbao Yan

doi:10.1117/12.2674882

2 May 2023 Embedded implementation method of complex equipment health state prediction based on LightGBM

Sen Wang, Wei Niu, Danning Zhang, Tanbao Yan

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420P (2023) https://doi.org/10.1117/12.2674882
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

In order to predict the key health parameters of complex equipment, this paper constructs an ensemble learning model based on LightGBM with a data-driven idea. Taking the hydraulic system as a typical research object, we have realized the prediction of its flow parameters. Under multiple working conditions, the R² reached 0.9988, and the prediction accuracy and time cost were excellent. Finally, using the relevant tool chain, we deploy the prediction model in the embedded environment to verify the effectiveness of the method.

Citation Download Citation

Sen Wang, Wei Niu, Danning Zhang, and Tanbao Yan "Embedded implementation method of complex equipment health state prediction based on LightGBM", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420P (2 May 2023); https://doi.org/10.1117/12.2674882

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