It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

中文翻译：

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基于自适应批归一化算法的深度卷积神经网络的轴承故障诊断开发

提取包含故障信息的特征对旋转机械进行故障诊断至关重要。以前使用深度卷积神经网络（CNN）进行的许多工作在从检测到的信号的特征提取中查找故障信息方面都取得了出色的性能。但是，它们可能会耗时且通用性差。为了避免此类复杂网络的高计算资源需求，本文开发了一种与自适应批归一化（ABN）算法（ABN-CNN）集成的CNN。它使用了基层的大规模卷积核和多维3×1小卷积核。因此，可以在噪声和负载变化环境下实现快速收敛和较高的识别精度，用于轴承故障诊断。