全矢深度学习在轴承故障诊断中的应用

为了应对日趋庞杂的故障监测系统数据,针对单通道信号存在的信息遗漏以及传统智能诊断手工提取特征的复杂性和不通用性,提出了全矢深度学习滚动轴承智能诊断方法。首先,用全矢谱融合双通道的振动信号,得到全矢融合后的主振矢数据,克服了单通道振动信号信息不完整的缺点;然后,在此基础上构建全矢深度神经网络,结合稀疏编码和去噪编码算法,自适应地提取故障特征。最后,使用反向传播算法进行网络参数整体微调。试验结果表明,该方法能够自适应地提取更为有效的故障特征,提高了故障诊断准确率和稳定性,改善了传统方法的复杂流程。

Application of Full Vector Deep Learning in Bearing Fault Diagnosis

To handle the numerous and jumbled data from fault monitoring systems,considering information missing with the single channel signal and the complexity and non generality of traditional intelligent diagnostic manual extracting features,a method named full vector deep learning in intelligent fault diagnosis of rolling bearing is put forward.Firstly,full vector spectrum is used to fuze the binary channel signal,the main vibration vector data after full vector fusion is acquired,the disadvantage of incomplete single channel vibration signal is overcome.Then,a full-vector deep neural network is built on this basis,combining sparse coding and de-noising coding algorithm,the fault features can be extracted automatically.Finally,the back-propagation algorithm is used to fine-tune the whole network.Experimental results show that the presented method can extract more effective fault features automatically,the classification accuracy and stability of diagnosis are improved,and the complex process of traditional methods is improved.