Wind power gearbox fault diagnosis of BP neural network based on improved Sparrow algorithm

Quancai Li; Sixia Fan

doi:10.1117/12.3024535

13 May 2024 Wind power gearbox fault diagnosis of BP neural network based on improved Sparrow algorithm

Quancai Li, Sixia Fan

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131595H (2024) https://doi.org/10.1117/12.3024535
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

It is difficult to identify the faults on the surface and root of the fan gearbox, resulting in low diagnostic accuracy. As an important part of the wind turbine, the abnormal vibration of the gear is easy to cause great losses, in order to improve the specific fault identification rate and reduce the fault. The following aspects are used to improve the sparrow algorithm. First, the homogeneous distribution Tent chaotic model is introduced to initialize the population distribution to improve the uniformity of the population distribution, then the Gaussian variation is used to add the random distribution to the newly generated individuals, and finally the adaptive weight is used to determine the value of the adjustment weight from the adaptation according to the population search state. The optimized sparrow algorithm can avoid falling into the local optimal solution and improve the convergence speed. By using the final conclusion of the improved sparrow algorithm, the accuracy of fault detection can be improved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Quancai Li and Sixia Fan "Wind power gearbox fault diagnosis of BP neural network based on improved Sparrow algorithm", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131595H (13 May 2024); https://doi.org/10.1117/12.3024535

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