In order to eliminate the influence of parameter predefined caused by manual feature extraction, achieve fast feature extraction, and improve the recognition rate of fault diagnosis, a 2-D convolution neural network (CNN) method for fault diagnosis of conventional circuit breaker contact system is proposed. First, by introducing the data preprocessing method of continuous wavelet transform (CWT), the nonlinear and nonstationary original vibration signal is transformed into a time–frequency image to extract the transformed image features. Second, the convolutional layer module in the AlexNet model is combined with the network in network (NIN) module, and the global average pooling (GAP) layer is adopted to replace the fully connected (FC) layer, which realizes the improvement of the traditional AlexNet model. Then, an improved Adam optimization algorithm, namely, AMSGrad, is adopted to solve the problem that the Adam optimization algorithm may not converge or produce local optimization during model training. Finally, the preprocessed time–frequency image is taken as the input of the improved AlexNet model and through the supervised adjustment of network parameters, the fault diagnosis of the contact system for the conventional circuit breaker is realized accurately.

中文翻译：

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基于时频分析和改进AlexNet的常规断路器触头系统故障诊断

为消除人工特征提取对预定义参数的影响，实现快速特征提取，提高故障诊断的识别率，采用二维卷积神经网络（CNN）方法对常规断路器触头系统进行故障诊断。建议的。首先，通过引入连续小波变换(CWT)的数据预处理方法，将非线性非平稳的原始振动信号转化为时频图像，提取变换后的图像特征。其次，将AlexNet模型中的卷积层模块与network in network（NIN）模块相结合，采用全局平均池化（GAP）层代替全连接（FC）层，实现了对传统的改进AlexNet 模型。然后，采用改进的Adam优化算法AMSGrad来解决Adam优化算法在模型训练过程中可能不收敛或产生局部优化的问题。最后，将预处理后的时频图像作为改进AlexNet模型的输入，通过网络参数的有监督调整，准确实现对常规断路器触头系统的故障诊断。