针对传统故障诊断方法中多传感器数据融合技术难度大、特征提取困难等问题,提出了一种基于深度卷积网络的多传感器信号故障诊断方法,通过构建测量数据帧进行卷积计算实现多通道数据的自然融合,利用深度网络结构实现高层特征的自动提取和分类,从而高效地实现了故障分类诊断;经分别采用小规模数据集REF和大规模故障数据集BI02进行实验验证,均取得了较高的故障识别准确率,具有很强的工程应用价值.%This paper presents a multi-sensor fault diagnose method based on convolutional neural networks,which utilizes the convolutional core to fuse the different type of measurement data via constructing the measurement data frame.Meanwhile,the high-level features are abstracted automatically from original signal data,and then fault type can be specified according to the output of classifier.As result,the fault recognition achieves high accuracy in treating both a small-scale dataset REF and a large-scale dataset BI02,which shows a significant effect and strong application value.
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