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国外文献 > 1.轴承 > 2. 轴承类型与部件 > 滚动轴承

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标 题： A Bearing Fault Diagnosis Method Based on an Improved Depth Residual Network
基于改进深度残差网络的轴承故障诊断方法
作 者： Litai Yi
Mingliang Liu
关键词： Fault diagnosis  [故障诊断]
Rolling bearings  [ 滚动轴承]
Residual learning  [ 残差学习]
Attention mechanisms  [ 注意力机制]
出版年： 2022
文集名： 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC 2022)
中国自动化学会第37届青年学术年会（YAC 2022）
会议名： 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC 2022)
中国自动化学会第37届青年学术年会（YAC 2022）
届 次： 37th
摘 要： To solve the problem that it is difficult to accurately identify bearing damage degree under strong noise, an Improved Deep Residual Network based on Normalization-based Attention Module (IDRNA) is proposed for bearing fault diagnosis. Firstly, the bearing vibration signals are preprocessed to obtain data samples, and then it is divided into training and testing subsets. After that a parallel 1x1 convolution branch is added for each convolution operation in the residual block, and Normalization-based Attention Module is introduced at the end of the residual block to suppress some unimportant features to obtain the improved deep residual model. Use the training set to train the enhanced model. And the noisy signal is input to the trained model for the fault degree identification. Finally, to verify the feasibility of the model, we compare it with some other models and get good results.

为解决在强噪声环境下难以准确识别轴承损伤程度的问题，提出了一种基于归一化注意力模块的改进型深度残差网络（IDRNA）用于轴承故障诊断。首先，对轴承振动信号进行预处理以获取数据样本，然后将其划分为训练集和测试集。接着，在残差块的每个卷积操作中添加并行的1x1卷积分支，并在残差块末尾引入归一化注意力模块，以抑制一些不重要的特征，从而构建出改进的深度残差模型。使用训练集对增强后的模型进行训练，再将带噪声的信号输入已训练好的模型进行故障程度识别。最后，通过与其它模型进行对比，验证了该模型的可行性，并取得了良好的结果。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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