As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

中文翻译：

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基于小波变换和卷积神经网络的轴承智能故障诊断

作为现代机械设备的重要组成部分之一，滚动轴承的准确实时诊断尤为重要。传统的故障诊断方法具有诊断精度低，故障特征提取困难等缺点。本文提出了一种将小波变换（WT）和可变形卷积神经网络（D-CNN）相结合的方法，以实现端到端滚动轴承的精确实时故障诊断。滚动轴承的振动信号作为监控目标。首先，采用正交匹配追踪（OMP）算法去除谐波信号，保留冲击信号和噪声。其次，利用小波分析通过时频变换得到信号的时频图。最后，D-CNN用于特征提取和分类。实验结果表明，该方法在各种故障模式下的准确率均达到99.9％，可以准确识别滚动轴承的故障。