基于改进深层极限学习机的故障诊断方法

提出一种新的基于稀疏和近邻保持理论深层极限学习机（sparsity and neighborhood preserving deep extreme learning machines，简称 SNP-DELM））的滚动轴承故障诊断方法。首先，将极限学习机（extreme learning machine，简称ELM）与自编码器（autoencoder，简称AE）相结合，提出一种ELM-AE的结构，利用自编码器对极限学习机的隐含层进行分层；其次，将稀疏与近邻思想融入深层网络中，在投影过程中，通过稀疏表示保持数据的全局结构，通过近邻表示保持数据的局部流形结构，无监督地逐层提取数据的深层特征；最后，通过监督学习求解最小二乘进行分类诊断。将该方法用于风机滚动轴承故障诊断实验，并与ELM、堆叠降噪自编码器（stacked autoencoder，简称SAE）、深层极限学习机（deep extreme learning machine，简称DELM）、卷积神经网络（convolution neural network，简称CNN）等方法进行对比，实验结果表明，SNP-DELM算法相对于现有的几种算法具有更高的准确率和稳定性。

Research on Mechanical Fault Diagnosis Method Based on Improved Deep Extreme Learning Machine

In this paper, a new sparsity and neighborhood preserving-deep extreme learning machine (SNPDELM) based on sparsity and neighbor preserving theory is proposed for fault diagnosis of rolling bearings. Firstly, a structure of extreme learning machine-autoencoder (ELM-AE) is constructed by combining the extreme learning machine with the auto-encoder, and the hidden layer of the extreme learning machine is layered by auto-encoder. Then, the theories of sparse representation and neighbor representation are introduced to the deep network. During the projection process, the global structure of the data is maintained by sparse representation and the local manifold structure of the data is maintained by the neighbor representation. The deep features of the data are extracted without supervision successively. Finally, the data are classified by solving least squares. This method is applied to diagnose faults of the fan rolling bearings. Compared with other algorithms such as deep extreme learning machine (DELM), extreme learning machine (ELM), stacked autoencoder (SAE), and convolution neural network (CNN), the experimental results show that the algorithm proposed in this paper has higher accuracy and stability.