Bearing is the most basic component of motor, and prone to failure. Bearing fault diagnosis is paramount for improving the reliability and safety in motor-drive systems. Therefore, convolutional neural network (CNN) is proposed with Logistic chaotic map and its corresponding fault diagnosis approach, which can effectively advance the accuracy of bearing fault diagnosis. Specifically, the Logistic chaotic map and Sigmoid function are combined into a non-monotonic excitation function, which is employed to the full connection layer of the CNN. The proposed chaotic CNN can solve two issues that the conventional neural network inclines to get the local minimum value and the gradient of Sigmoid excitation function disappears. It is applied to fault data from the center of Western Reserve University and from the American Society for Mechanical Failure Prevention technology (in noiseless and noisy conditions). The results indicate the diagnosis accuracy of the algorithm outperforms other classical bearing diagnosis algorithms. Moreover, the chaotic CNN exhibits better anti-noise performance.

中文翻译：

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基于Logistic混沌图卷积神经网络的轴承故障诊断

轴承是电机最基本的部件，也是容易发生故障的部件。轴承故障诊断对于提高电机驱动系统的可靠性和安全性至关重要。因此，结合Logistic混沌图提出卷积神经网络（CNN）及其相应的故障诊断方法，可以有效提高轴承故障诊断的准确性。具体来说，将Logistic混沌图和Sigmoid函数组合成非单调激励函数，应用于CNN的全连接层。所提出的混沌CNN可以解决传统神经网络容易获得局部极小值和Sigmoid激励函数梯度消失的两个问题。它应用于来自西储大学中心和美国机械故障预防技术协会的故障数据（在无噪声和噪声条件下）。结果表明该算法的诊断精度优于其他经典轴承诊断算法。此外，混沌CNN表现出更好的抗噪声性能。