基于改进残差卷积自编码网络的类自适应旋转机械故障诊断

Rotary machine fault diagnosis based on improved residual convolutional auto-encoding network and class adaptation

针对旋转机械传感器信号样本有限影响深层网络模型训练学习的问题,提出一种结合改进残差卷积自编码网络与类自适应方法的故障诊断模型应对小样本数据。首先将少量已标记的源域数据和目标域数据创建为成对样本,并设计一种改进的一维残差卷积自编码网络对两种不同分布的原始振动信号进行特征提取;其次,利用最大均值差异(MMD)减小分布差异,并将两个域同一故障类别的数据空间映射到一个共同的特征空间,最终实现准确的故障诊断。实验结果表明,与微调、域自适应等方法相比,所提模型能够有效提高不同工况、微量已标记的目标域振动数据下的故障诊断准确率。

Aiming at the insufficient deep network model training problem caused by limited rotary machine sensor signal samples,a fault diagnosis model combining improved residual convolutional auto-encoding network and class adaption method was proposed to deal with the data with small sample size.Firstly,paired samples were created by a small number of labeled source domain data and target domain data,and an improved one-dimensional residual convolutional auto-encoding network was designed to extract features from two types of original vibration signals with different distributions.Secondly,the Maximum Mean Discrepancy(MMD)was used to reduce the distribution difference,and the data space of the same fault category from two domains was mapped to a common feature space.Finally,the accurate fault diagnosis was realized.Experimental results show that the proposed model is able to effectively improve the fault diagnosis accuracy of the target domain vibration data with few labels under different working conditions compared with the fine-tuning and domain adaptation methods.