摘要
针对传统滚动轴承故障识别算法存在的特征提取与选择困难的问题,提出了一种基于深度字典学习(DDL)的滚动轴承故障诊断方法。首先,利用传感器采集了不同工况下的滚动轴承故障振动数据,并利用字典学习的稀疏性约束逐层学习了轴承故障数据中的典型结构特征;然后,借鉴深度学习的“逐层特征提取”思想,根据故障样本结构构造了深度故障字典,将故障样本输入深度故障字典,根据样本的重建误差确定了故障类别;最后,以滚动轴承试验台为对象测试了DDL模型的有效性。研究结果表明:采用该方法得到的滚动轴承故障识别准确率达到99.28%,训练时间仅为765 s;相比于卷积神经网络、循环神经网络等深度学习方法,该方法在故障识别准确率方面和训练速度方面具有较大优势;DDL方法利用驱动字典,可以自动提取出轴承振动信号样本中的故障特征,同时,深度字典结构使所提取的故障特征具有较好的层次性,符合人们对故障的直观认识。
Aiming at the difficulty of feature extraction and feature selection of traditional rolling bearing fault recognition algorithms,a rolling bearing fault diagnosis method based on deep dictionary learning(DDL)was proposed.Firstly,the rolling bearing fault vibration data under different working conditions were collected using sensor and the sparsity constraint was implemented by DDL to learn the typical structural features in the fault data layer by layer.Secondly,drawing on the idea of“layer by layer feature extraction”of deep learning method,the deep fault dictionary was constructed according to the fault sample's structure.And the fault samples were fed into the deep fault dictionary to determine the fault category according to the reconstruction error of the samples.Finally,the effectiveness of the DDL model was tested on the rolling bearing test bench.The results of the research indicate that the fault recognition rate of rolling bearing of the proposed deep dictionary learning method reaches 99.28% and the training time is only 765 s,which have great advantages in fault recognition accuracy and training time comparing with other deep learning methods such as convolutional neural network and recurrent neural network.The deep dictionary learning method employs the sparse constraint driving dictionary to automatically extract the fault features in the vibration signal samples,while the deep dictionary structure makes the extracted fault features have better hierarchical and physical meaning,which in line with people's intuitive understanding of the fault.
作者
余阿东
YU A-dong(School of Automotive and Electrical Engineering,Xinyang Vocational and Technical College,Xinyang 464000,China)
出处
《机电工程》
CAS
北大核心
2022年第2期231-237,共7页
Journal of Mechanical & Electrical Engineering
基金
河南省哲学社会科学规划年度项目(2021BJJ082)。
关键词
滚动轴承
故障识别
深度字典学习
稀疏表示
rolling bearing
fault recognition
deep dictionary learning(DDL)
sparse representation
