基于MTF-CNN的轴承故障诊断研究

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (2) : 126-131.

论文

基于MTF-CNN的轴承故障诊断研究

赵志宏1,2，李春秀1，窦广鉴1，杨绍普2

作者信息 +

Bearing fault diagnosis method based on MTF-CNN

ZHAO Zhihong1,2，LI Chunxiu1，DOU Guangjian1，YANG Shaopu2

Author information +

文章历史 +

摘要

轴承故障诊断对保证机械设备的安全十分重要。近年来，数据驱动的故障诊断方法得到了研究者的关注。与传统的依赖于专家经验的故障特征提取方法不同，深度学习方法可以实现端到端自动故障特征提取与分类。针对一维信号作为卷积神经网络(convolutional neural network ,CNN)输入时无法充分利用数据间的相关信息的问题，提出一种基于MTF-CNN 的轴承故障诊断方法。利用Markov Transition Field( MTF) 对采集到的振动信号进行编码，根据数据之间的转移概率得到不同时间间隔内的数据相关性并生成相应特征图，之后将其输入卷积神经网络完成特征的提取并进行故障分类。采用凯斯西储大学轴承数据对模型进行验证，实验结果表明该模型达到99.8%以上的故障诊断准确率，与其他图像编码方式相比获得了较好的泛化性能。

Abstract

Bearing fault diagnosis is very important to ensure the safety of mechanical equipment. In recent years, data-driven fault diagnosis methods have attracted the attention of researchers. Unlike the traditional fault feature extraction methods that rely on expert experience, the deep learning method can realize end-to-end automatic fault feature extraction and classification. In response to the problem that one-dimensional signal cannot fully exploit the relevant information between data when used as input to a convolutional neural network (CNN), a bearing fault diagnosis method based on a MTF-CNN is proposed. The collected vibration signals are encoded by the Markov transition field(MTF), the data correlation in different time intervals is obtained according to the transfer probability between data, and the corresponding feature map is generated. Then, it is input to a CNN to complete feature extraction and fault classification. The model is verified by the bearing dataset of Case Western Reserve University. The experimental results show that the fault diagnosis accuracy of the model is over 99.8%, and better generalization performance is obtained compared to other image coding methods.

导出引用

赵志宏1,2，李春秀1，窦广鉴1，杨绍普2. 基于MTF-CNN的轴承故障诊断研究[J]. 振动与冲击, 2023, 42(2): 126-131

ZHAO Zhihong1,2，LI Chunxiu1，DOU Guangjian1，YANG Shaopu2. Bearing fault diagnosis method based on MTF-CNN[J]. Journal of Vibration and Shock, 2023, 42(2): 126-131

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