Fault diagnosis of wind turbine bearings based on improved dung beetle
optimizer optimized LSTM
Abstract
Due to the nonlinearity, non-stationarity and noise interference of the
rolling bearing fault signals of wind turbines, the extraction of
features is challenging. This paper proposes a fault diagnosis method
for wind turbine bearings based on the improved dung beetle optimizer
(IDBO) optimized Long Short-Term Memory (LSTM) network. Firstly, Levy
flight strategy and T-distribution perturbation strategy are integrated
into the traditional dung beetle optimizer (DBO) to optimize the
algorithm. The optimized IDBO algorithm solves the problems of low
convergence accuracy and easy entrapment into local optima in the
traditional dung beetle algorithm. Secondly, the IDBO algorithm is
combined with the long short-term memory method to build the IDBO-LSTM
fault diagnosis model. The advantage of fewer parameters in this
algorithm enables the model to reduce the overall number of parameters
and computational complexity while ensuring the accuracy of temporal
feature extraction. Finally, this pa-per takes the bearing open dataset
of Case Western Reserve University as an example to compare the proposed
method with other models through optimization tests. The experimental
results show that the IDBO-LSTM model outperforms other models, with a
fault diagnosis accuracy rate of 98.3%. This demonstrates the
superiority of the proposed improved dung beetle optimizer and
effectively improves the accuracy of fault diagnosis for rolling
bearings of wind turbines.