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A Rub-Impact Recognition Method Based on Improved Convolutional Neural Network

机译：一种基于改进卷积神经网络的耐摩擦识别方法

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摘要

Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation. Due to the convolutional pooling operations of CNN, coarse-grained and edge information are lost, and the top-level information dimension in CNN network is low, which can easily lead to overfitting. To solve the above problems, we first propose the use of sound spectrograms and their differential features to construct multi-channel image input features suitable for CNN and fully exploit the intrinsic characteristics of the sound spectra. Then, the traditional CNN network structure is improved, and the outputs of all convolutional layers are connected as one layer constitutes a fused feature that contains information at each layer, and is input into the network's fully connected layer for classification and identification. Experiments indicate that the improved CNN recognition algorithm has significantly improved recognition rate compared with CNN and dynamical neural network (DNN) algorithms.
机译：基于模态声学发射（AE）的理论，当卷积神经网络（CNN）用于识别转子摩擦碰撞故障时，训练数据具有小的样本大小，并且AE声音段属于单通道信号具有较少像素级信息和强大的本地相关性。由于CNN的卷积汇总操作，粗粒度和边缘信息丢失，CNN网络中的顶级信息维度低，这很容易导致过度拟合。为了解决上述问题，我们首先提出了声光谱图和它们的差动特征来构造适合于CNN的多通道图像输入特征并充分利用声光谱的内在特征。然后，改进了传统的CNN网络结构，并且所有卷积层的输出连接为一层构成包含在每个层处的信息的融合特征，并且被输入到网络的完全连接层以进行分类和识别。实验表明，与CNN和动态神经网络（DNN）算法相比，改进的CNN识别算法具有显着提高的识别率。

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