基于堆栈降噪自编码的轴承故障诊断方法

Fault Diagnosis Method of Bearing Based on Stacked Denoising Autoencoder

针对传统轴承故障智能诊断中特征学习困难,且需要掌握大量的信号处理方法和诊断经验,提出直接从原始数据出发对轴承故障状态进行分类识别的新方法。该方法通过深度学习利用原始振动数据训练堆栈自编码网络,由于免除了智能诊断的显式特征提取阶段,从而能够减少人工参与因素,摆脱了对大量信号处理技术与诊断经验的依赖。试验结果显示:所提出的方法能对轴承故障识别率达到97%,具有较好的识别能力,能够完成故障特征的自适应提取,增强了机械故障诊断的智能性。

In order to solve the problem of feature learning in traditional bearing fault diagnosis,which were necessary to master a large number of signal processing methods and diagnostic experience,a new identification method is proposed to classify the bearing fault states directly from the original data. The method was used of the original vibration data to train the stacked denoising autoencoder network. Due to the elimination of the explicit feature extraction phase of intelligent diagnosis,the artificial participation factors could be reduced and the dependence on a large number of signal processing technology and diagnosis experience was gotten rid of. The experimental results show that the proposed method has a good recognition ability and practical significance to the bearing fault identification rate of 97%,and can achieve adaptive extraction of fault feature,which enhances the intelligence of machinery fault diagnosis.