基于深度网络的滚动轴承智能故障诊断

Intelligent fault diagnosis of rolling bearings based on deep network

针对变工况环境中滚动轴承的源域与目标域数据分布不同及目标域样本不含标签的问题,提出一种基于深度自适应迁移学习网络(DATLN)的滚动轴承故障诊断模型。首先,搭建领域共享特征提取网络,采用多尺度卷积神经网络(MSCNN)抑制噪声的干扰,进而有效提取振动信号中蕴含的局部故障信息;其次,结合双向长短时记忆网络(BiLSTM)进一步学习局部故障信息中的时间特征;最后,引入迁移学习,以域对抗(DA)训练结合自适应联合分布(AJD)度量构建域自适应模块,通过最大化域分类损失和最小化AJD距离,实现源域与目标域特征样本对齐。在开源CWRU数据集与机械故障平台实测数据集上分别进行抗噪实验和迁移实验。抗噪实验表明:①在无噪声环境下,MSCNN-BiLSTM网络的识别准确率均达到99%以上,说明其具有较好的特征提取能力;②MSCNN-BiLSTM,LeNet-5,MSCNN和BiLSTM四种网络的识别准确率随着噪声强度的增强而降低;③在3,5,10 dB噪声环境下,MSCNN-BiLSTM网络的平均识别准确率比LeNet-5,MSCNN和BiLSTM网络的平均识别准确率均高,说明MSCNN-BiLSTM网络具有较好的抗噪声干扰性能;④MSCNN-BiLSTM网络在无噪声环境和3 dB噪声环境下,均最先达到收敛且波动较小。迁移实验表明:①在无标签目标域数据集上,DA+AJD方法的平均识别准确率为97.36%,均高于Baseline,迁移成分分析(TCA),域对抗神经网络(DANN)的识别准确率;②在测试集混淆矩阵上,DA+AJD方法仅有1个样本被错误识别,表明基于域适应的DA+AJD方法具备更好的故障迁移诊断性能;③利用t-SNE算法对处理后的源域与目标域特征样本进行可视化,DA+AJD方法只有少量目标域的滚动体故障和外圈故障特征样本被错误对齐到源域的内圈故障特征样本区域,说明DA+AJD方法可有效减少源域与目标域的边缘分布和条件分布差异,进而达到更好的特征样本对齐效果。

In order to solve the problem that the data distribution of the source domain and the target domain of rolling bearing is different in the variable working condition environment and the samples of the target domain do not contain labels,a fault diagnosis model of the rolling bearing based on the deep adaptive transfer learning network(DATLN)is proposed.Firstly,a domain-shared characteristic extraction network is built,and multiscale convolutional neural network(MSCNN)is used to suppress noise interference,so as to effectively extract local fault information contained in vibration signals.Secondly,combined with a bi-directional long short-term memory network(BiLSTM),the temporal characteristics in the local fault information are further learned.Finally,transfer learning is introduced to build a domain adaptive module with domain adversarial(DA)training combined with adaptive joint distribution(AJD)metrics.By maximizing the domain classification loss and minimizing the AJD distance,the source and target domain characteristic samples are aligned.The anti-noise experiment and transfer experiment are carried out on the open source CWRU data set and the measured data set of the mechanical fault platform respectively.The anti-noise experiments show the following points.①The identification accuracy of MSCNN-BiLSTM network is above 99%in the noise-free environment,which shows that MSCNN-BiLSTM network has a good characteristic extraction capability.②The identification accuracy of MSCNN-BiLSTM,LeNet-5,MSCNN and BiLSTM decreases with the increase of noise intensity.③Under the noise environment of3,5 and 10 dB,the average identification accuracy of MSCNN-BiLSTM network is higher than that of LeNet-5,MSCNN and BiLSTM networks,indicating that MSCNN-BiLSTM network has better anti-noise interference performance.④The MSCNN-BiLSTM network converges first with less fluctuation in both the noise-free environment and the 3 dB noise environment.The transfer experiments show the following points.①The average identification accuracy of DA+AJD method is 97.36%on unlabeled target domain dataset,which is higher than that of Baseline,transfer component analysis(TCA)and domain adversarial neural network(DANN).②On the test set confusion matrix,only one sample of the DA+AJD method is incorrectly identified,indicating that the DA+AJD method based on domain adaptation has better fault transfer diagnosis performance.③The t-SNE algorithm is used to visualize the processed source and target domain characteristic samples.The DA+AJD method only has a small number of rolling element fault and outer ring fault characteristic samples in the target domain that are incorrectly aligned to the inner ring fault characteristic samples area in the source domain.This result indicates that the DA+AJD method can effectively reduce the edge distribution and conditional distribution differences between the source domain and the target domain,and thus achieves better characteristic sample alignment.