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Existing single-channel networks have poor noise immunity during fault diagnosis of rotating machinery due to the many noises associated with the operation of rotating machinery. To address this problem, a two-channel input LetNet-5 convolutional neural network model incorporating a parallel mechanism was proposed. Case Western Reserve University bearing dataset was used for the model plausibility check process, based on which Gaussian white noise with a signal-to-noise ratio of -10 dB was added to simulate the real noise situation. The short-time Fourier transform was used to process the motor fan-side and drive-side vibration data, and the resulting time-frequency images were passed to a two-channel input LetNet-5 convolutional neural network for training and learning. The results show that, the dual-channel input LetNet-5 convolutional neural network model is able to capture the fault features in a strong noise environment well, it has higher efficiency and accuracy than the multi-scale feature fusion residual model, the multimodal coupled input neural network model, the conventional K-nearest neighbour and decision tree model and the single-channel input LetNet-5 convolutional neural network model.

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针对旋转机械运行过程中伴随着诸多噪声，现有单通道网络在旋转机械故障诊断过程中抗噪性较差的问题，提出了一种加入并联机制的双通道输入LetNet-5卷积神经网络模型。模型合理性检验过程采用了凯斯西储大学轴承数据集，在此基础上，添加信噪比为–10 dB的高斯白噪声模拟真实噪声情形；采用短时傅里叶变换将电机风扇端和驱动端振动数据进行处理，获得的时频图像传递至双通道输入的LetNet-5卷积神经网络进行训练学习。研究结果表明：双通道输入LetNet-5卷积神经网络模型能够良好捕捉到强噪声环境下的故障特征；相比于多尺度特征融合残差模型、多模态耦合输入神经网络模型、传统的K近邻与决策树模型及单通道输入LetNet-5卷积神经网络模型，双通道输入LetNet-5卷积神经网络具有更高的效率和精度。

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付忠广（1963），男，博士，教授，主要研究方向为电站机组运行优化及复杂热力系统建模、大数据与人工智能、旋转机械故障诊断等，fzg@ncepu.edu.cn。

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付忠广（1963），男，博士，教授，主要研究方向为电站机组运行优化及复杂热力系统建模、大数据与人工智能、旋转机械故障诊断等，fzg@ncepu.edu.cn。

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