基于双向生成对抗网络的滚动轴承智能诊断方法

Intelligent diagnosis method of rolling bearing based on BiGAN

滚动轴承是旋转机械中的关键部件,直接影响设备的可靠性。人工智能的发展在轴承故障诊断领域取得了令人瞩目的成就。然而,滚动轴承数据集的不平衡(正常样本远丰富于故障样本)会导致诊断模型精度较低。为了解决这个问题,本文提出了一种基于双向生成对抗网络(BiGAN)的故障诊断方法。首先,通过集合经验模式分解对信号进行去噪,使其自动分配到一个合适的参考尺度,并避免模态混叠。其次,构建含有梯度惩罚项的BiGAN模型,利用单样本离差标准化方法稳定模型训练过程,实现故障样本扩充。最后,基于增强的训练集建立具有批归一化、最大池化层的卷积神经网络(CNN)诊断模型。实验结果表明,该方法提高了故障诊断的准确性和鲁棒性。

Rolling bearing is a critical component in the rotating machinery,which directly affects the reliability of the equipment.The artificial intelligence-enabled bearing fault diagnosis model has achieved impressive successes over the years.However,rolling bearings’imbalanced data sets(normal samples are much larger than failure samples)degrade the diagnostic performance.To address this issue,a bidirectional generative adversarial network(BiGAN)based fault diagnosis method was proposed.First,the signal was denoised via the ensemble empirical mode decomposition(EEMD)to automatically distribute it to a suitable reference scale and avoid modal aliasing.Then,the BiGAN model with gradient penalty term was constructed to expand the fault samples,where the min-max normalization was included.Finally,based on the enhanced training set,the convolutional neural network was established with batch normalization and maximum pooling layers.Experimental results proved that the proposed method improved fault diagnosis accuracy and robustness.