In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the vibration signals of rotating machine at different scales, and obtains permutation entropy (PE) to construct feature vector sets. Then, considering the structure and spatial information between different sensor measurement points, MCFCNN constructs multiple channels in the input layer according to the number of sensors, and each channel corresponds to the MPE feature sets of different monitored points. MCFCNN uses convolutional kernels to learn the features of each channel in an unsupervised way, and fuses the features of each channel into a new feature map. At last, multi-layer perceptron is applied to fuse multi-channel features and identify faults. Through the health monitoring experiment of planetary gearbox and rolling bearing, and compared with single channel convolutional neural networks (CNN) and existing CNN based fusion methods, the proposed method based on MPE and MCFCNN model can diagnose faults with high accuracy, stability, and speed.

中文翻译：

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使用多尺度排列熵和卷积神经网络的旋转机械故障诊断

针对现有旋转机故障诊断方法在单尺度信号分析中的局限性，提出了一种基于多尺度排列熵（MPE）和多通道融合卷积神经网络（MCFCNN）的故障诊断方法。首先，MPE定量分析不同尺度下旋转机械的振动信号，得到置换熵（PE）来构建特征向量集。然后，考虑到不同传感器测量点之间的结构和空间信息，MCFCNN根据传感器数量在输入层构建多个通道，每个通道对应不同监测点的MPE特征集。MCFCNN使用卷积核以无监督的方式学习每个通道的特征，并将每个通道的特征融合成一个新的特征图。最后，应用多层感知器融合多通道特征并识别故障。通过行星齿轮箱和滚动轴承的健康监测实验，与单通道卷积神经网络（CNN）和现有的基于CNN的融合方法相比，所提出的基于MPE和MCFCNN模型的方法可以高精度、稳定、快速地诊断故障.