基于改进一维卷积神经网络的滚动轴承故障识别

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (3) : 216-223.

论文

王琦，邓林峰，赵荣珍

作者信息 +

Fault recognition of rolling bearing based on improved 1D convolutional neural network

WANG Qi, DENG Linfeng, ZHAO Rongzhen

Author information +

文章历史 +

摘要

滚动轴承的故障识别对于防止旋转机械系统故障恶化并保证其安全运行具有重要意义。针对现有智能诊断模型参数多、识别效率低的问题，提出一种基于改进一维卷积神经网络的滚动轴承故障识别(FRICNN–1D)方法。通过引入1×1卷积核增强一维卷积神经网络模型的非线性表达能力；并用全局平局池化层代替传统卷积神经网络中的全连接层，以降低模型参数和计算量，且防止过拟合现象。实验结果表明，该方法可以准确识别滚动轴承不同故障状态，具有一定的工程实际应用潜力。

Abstract

Fault recognition of rolling bearings is largely significant to prevent the deterioration of the rotating machinery system and to guarantee its safe operation. Aiming at the problem that the used intelligent diagnosis models usually have too many parameters and low recognition efficiency, a rolling bearing fault recognition method based on improved one-dimensional convolutional neural network(FRICNN–1D) is proposed in this paper. For one thing, the 1×1 convolution kernel is introduced to enhance the nonlinear expression ability of the one-dimensional convolutional neural network model; for another, the global average pooling layer is used to replace the fully connection layer in the traditional convolution neural network, so as to reduce the model parameters and the amount of calculation and prevent over fitting phenomenon. The experimental results show that the proposed method can accurately recognize different fault status of a real rolling bearing and has particular potential in engineering application.

导出引用

王琦，邓林峰，赵荣珍. 基于改进一维卷积神经网络的滚动轴承故障识别[J]. 振动与冲击, 2022, 41(3): 216-223

WANG Qi, DENG Linfeng, ZHAO Rongzhen. Fault recognition of rolling bearing based on improved 1D convolutional neural network[J]. Journal of Vibration and Shock, 2022, 41(3): 216-223

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