基于融合型深度学习的滚动轴承亚健康识别算法

Rolling bearing sub-health recognition algorithm based on fusion deep learning

深度学习模型增加了隐含层的层数,使得该模型在语音识别、图像视频分类等方面取得了不错的效果;但建立一个适合特定对象的模型需要大量的数据集来训练,而且需要较长时间才能获得合适的权重和偏置,为此提出一种基于深度自动编码器-相关向量机网络模型的滚动轴承亚健康诊断方法。首先,采集滚动轴承振动信号并进行傅里叶变换和归一化处理;其次,设计改进的自动编码器-稀疏边缘降噪自动编码器,结合了稀疏自动编码器和边缘降噪自动编码器的特点;接着建立深度自动编码器-相关向量机网络模型,用有监督的函数对各个隐含层的参数进行微调,并利用相关向量机(RVM)进行训练;最后将得到的分类根据D-S证据理论融合并得出最终的分类结果。实验结果表明所提算法能有效提高滚动轴承"亚健康"状态的识别精度,纠正错误分类。

The deep learning model increases the number of hidden layers,which makes the model have a good effect on speech recognition,image video classification and so on. However,to establish a model suitable for a specific object,a large number of data sets are required to train it for a long time to get the appropriate weights and biases. To resolve the above problems,a sub-health diagnosis method for rolling bearing was proposed based on depth autoencoder-relevance vector machine network model. Firstly, the bearing vibration signal was collected and transformed by Fourier transform and normalization. Secondly,the improved automatic encoder,named sparse edge noise reduction autoencoder,was designed,which combined the features of sparse automatic encoder and edge noise reduction automatic encoder. Then the depth autoencoder-relevance vector machine network model was designed,in which the supervised function was used to finely tune the parameters of each hidden layer,and it was trained by Relevance Vector Machine（ RVM）. Finally,the final classification results were obtained according to D-S（ Dempster-Shafer） evidence fusion theory. The experimental results show that the proposed algorithm can effectively improve the recognition precision of the＂sub-health＂state of the rolling bearing and correct the error classification.