基于多输入层卷积神经网络的滚动轴承故障诊断模型

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摘要针对滚动轴承信号易受噪声干扰和智能诊断模型鲁棒性差的问题，在一维卷积网络的基础上，提出基于多输入层卷积神经网络的滚动轴承故障诊断模型。相比传统卷积神经网络诊断模型，该模型具有多个输入层，初始输入层为原始信号，以最大化地发挥卷积网络自动学习原始信号特征的优势；同时可将谱分析数据在模型任意位置输入模型，以提升模型的识别精度和抗干扰能力。首先，通过滚动轴承模拟试验，进行可行性和有效性验证，同时与人工神经网络（Artificial Neural Network，ANN）、支持向量机（Support Vector Machine，SVM）和典型的卷积神经模型进行对比，证明了所提出模型的优势；然后，向测试集中加入噪声来检验模型的鲁棒性，并且运用增量学习方法提升模型在强噪声环境下的识别性能；最后，通过滚动轴承故障实例，验证模型的识别性能和泛化能力。试验结果表明，所提出的模型提升了传统卷积模型的识别率和收敛性能，并具有较好的鲁棒性和泛化能力。

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	昝涛1
	王辉 1
	刘智豪1
	王民1
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	高相胜1

关键词 ：深度学习, 卷积神经网络, 滚动轴承, 故障诊断

Abstract：Aiming at the problem that rolling bearings signal is susceptible to noise interference and the poor robustness of intelligent diagnosis model, a fault diagnosis model for rolling bearings based on multi-input layer convolutional neural network was proposed on the basis of one-dimensional convolutional network. Compared with the traditional convolutional neural network diagnosis model, the model had multiple input layers. The data of initial input layer was the original signal, in order to maximize the advantages of the convolutional network to automatically learn the original signal characteristics. The spectral analysis data could be input into the network at any position of the model, in order to improve the recognition accuracy and anti-jamming ability of the model. Firstly, through the simulation test of rolling bearing, the feasibility and validity of the proposed method were verified. Then, the robustness of the model was tested by adding noise to the test set, and the recognition performance of the model in strong noise environment was improved by using incremental learning method. Finally, through the example of rolling bearing fault, the recognition performance and generalization ability of the model were verified. The experimental results show that the proposed model can improve the recognition rate and convergence performance of the traditional convolutional model, and has good robustness and generalization ability.

Key words： deep learning convolutional neural network rolling bearing fault diagnosis

收稿日期: 2019-01-23 出版日期: 2020-06-15

引用本文:

昝涛1，王辉 1，刘智豪1，王民1，2，高相胜1. 基于多输入层卷积神经网络的滚动轴承故障诊断模型[J]. 振动与冲击, 2020, 39(12): 142-149.
ZAN Tao1,WANG Hui1,LIU Zhihao1,WANG Min1,2,GAO Xiangsheng1. A fault diagnosis model for rolling bearings based on a multi-input layer convolutional neural network. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(12): 142-149.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I12/142

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