基于改进深度残差网络的旋转机械故障诊断

Fault diagnosis of rotating machinery based on improved deep residual network

针对旋转机械工况复杂多变、有标签样本不足而导致的故障特征提取困难等问题,提出了一种用于旋转机械故障诊断的改进深度残差网络(improved deep residual network,IDRN)。首先,采集旋转机械一维振动信号进行数据预处理;然后,在深度残差网络的基础上引入了长短时记忆(long short-term memory,LSTM)网络,其中,LSTM网络可以有效捕捉故障的时序信息;在残差块中引入Dropout层提高了故障诊断的精度和收敛速度;最后在轴承与齿轮数据集上验证本文提出方法的有效性。实验结果表明,该方法在堆叠多层网络模型时,没有出现明显的网络退化现象,与当前广泛使用的几种诊断方法进行对比实验,表现出了较高的平均诊断精度和良好的适用性。

An improved deep residual network(IDRN)for fault diagnosis of rotating machinery is proposed to solve the problems of fault feature extraction difficulty caused by complex and variable working conditions and insufficient samples of labels.Firstly,one-dimensional vibration signals of rotating machinery are collected for data preprocessing.Then,long short-term memory(LSTM)network is introduced on the basis of the deep residual network,in which the time-series information of faults could be captured effectively.The Dropout layer is introduced into the residual block to improve the accuracy and convergence speed of fault diagnosis.Finally,the validity of the proposed method is verified on the data sets of bearings and gears.Experimental results show that there is no obvious network degradation phenomenon when the proposed method is used to stack multi-layer network models.Compared with several widely used diagnostic methods,the proposed method shows higher average diagnostic accuracy and good applicability.