改进数据增强方法在轴承故障诊断中的应用

Application of Improved Data Augment Method in Motor Bearing Fault Diagnosis

基于数据驱动的电机轴承故障诊断方法往往需要大量的故障样本才能获得理想的诊断效果,然而在实际的工业生产过程中故障样本往往难以获得。针对故障样本数量有限的问题,提出将压缩激励机制(SE)融合到辅助分类生成式对抗网络(ACGAN)的数据增强方法中,进一步提高生成样本和原始样本分布的一致性。经仿真验证,该方法相比于传统模型可以进一步提高故障诊断准确率,准确率最高可达99.82%。改进后的ACGAN可以更有效地学习原始样本的特征,从而进一步提高故障诊断准确率,具有收敛速度快、生成样本质量更为稳定的优点。

The fault diagnosis method of motor bearing based on data-driven often needs a large number of fault samples to obtain the ideal diagnosis effect.However,in actual industrial production process,the fault samples are often difficult to obtain.In order to solve the problem of limited number of fault samples,propose a data augment method which integrates the squeeze and exception(SE)mechanism into auxiliary classifier generative adversarial nets(ACGAN),so as to further improve the consistency of the generated samples and the original samples.The simulation results show that the proposed method can further improve the accuracy of fault diagnosis compared with the traditional model,and the highest accuracy can reach 99.82%.The improved ACGAN can learn the features of the original samples more effectively,which further improves the accuracy of fault diagnosis.It has the advantages of fast convergence speed and more stable quality of generated samples.