细菌觅食算法优化卷积神经网络的滚动轴承故障诊断

Bacterial Foraging Algorithms Optimize Rolling Bearing Fault Classification for Convolutional Neural Network

针对传统深度学习故障诊断方法在滚动轴承中诊断效果不理想的问题,提出一种细菌觅食优化算法(BFO)优化卷积神经网络(CNN)学习率使诊断效果提升的模型。在模型逐次迭代过程中,将CNN中的学习率参数带入BFO中,生成一个自适应的学习率,用于更新CNN的权重和偏置,使模型故障诊断效果达到最佳。通过实验证明基于细菌觅食算法优化的卷积神经网络训练的模型在分类精度上优于CNN训练的模型,并与CNN多种学习率对比,可将故障诊断准确率提升至97.25%,并提高了全局的收敛能力。

Aiming at the problem that the diagnosis effect of traditional deep learning fault diagnosis method is not satisfactory in rolling bearings,a model of bacterial foraging optimization algorithm(BFO)optimization convolutional neural network(CNN)learning rate improves the diagnostic effect.During the model iteration,the learning rate parameter in the CNN is brought into the BFO to generate an adaptive learning rate that is used to update the weights and biases of the CNN to achieve the best model fault diagnosis.Through experiments,it is proved that the model trained by the convolutional neural network optimized by the bacterial foraging algorithm is better than the CNN-trained model in terms of classification accuracy,and compared with the multiple learning rates of CNN,the fault diagnosis accuracy can be improved to 97.25%,which improves the overall convergence ability.