多传感器信息融合的轴承故障迁移诊断方法

Transfer learning method for shearer fault diagnosis under variable working conditions

在重型装备低速、重载、强噪声环境下,采用单一传感器难以全面获取轴承的故障诊断信息,导致故障识别率低、识别不稳定,致使变工况下轴承故障迁移诊断失效。针对以上问题,提出了一种多传感器信息融合的轴承故障迁移诊断方法。首先,结合传感器的通道数,构建了堆叠卷积神经网络(MCNNs)提取各个通道的故障特征;然后,在MCNNs中引入最小绝对收缩与选择算子(Lasso),并通过网络反向传播完成了特征权值的更新,从而获得了多通道特征的融合;最后,利用源域数据对模型进行了训练,提取了故障特征,并完成了特征融合,采用损失函数完成了模型参数的优化,将源域训练得到的模型结果作为目标域的初始模型,利用目标域样本对初始模型的参数进行了微调,从而完成了模型迁移;并进行了信息融合效果、方法对比以及传感器信息采集属性的性能实验。研究结果表明:传感器的安装位置对信息融合影响较大,MCNNs+Lasso方法具有较好的特征融合效果,平均迁移诊断精度为99.03%,部分精度可达99.97%,在多个变工况的迁移任务中表现出较高迁移精度和良好的泛化性能。

In the environment of low speed,heavy load and strong noise of heavy equipment,it is difficult for a single sensor to fully obtain bearing fault diagnosis information,resulting in low fault identification rate and unstable identification,resulting in bearing fault migration diagnosis failure under changing working conditions.Aiming at the above problems,a bearing fault migration diagnosis method based on multi-sensor information fusion was proposed.Firstly,multiple convolution neutral networks(MCNNs)was constructed combined with the number of sensor channels to extract the fault features of each channel.Then,the least absolute shrinkage and selection operator(Lasso)was introduced into MCNNs,and the feature weight was updated through network back propagation.Thus multi-channel feature fusion was realized.Finally,the source domain data was used to train the model and extract fusion features,and the model parameters were optimized through loss function.The model results obtained from the source domain training were taken as the initial model of the target domain,and the parameters of the initial model were fine-tuned through the target domain samples,so as to realize model migration.Finally,the performance experiments of information fusion effect,method comparison and sensor information acquisition attribute were done.The experimental results show that the sensor installation position has a great influence on information fusion,multiple convolution neutral networks combined with least absolute shrinkage and selection operator(MCNNs+Lasso)has a good feature fusion effect.The average diagnostic accuracy of migration is 99.03%,and the partial accuracy can reach 99.97%.The proposed method shows high migration accuracy and good generalization performance in multiple migration tasks with varying working conditions.