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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.