电子测量与仪器学报

赵小强,罗维兰.改进卷积 Lenet-5 神经网络的轴承故障诊断方法[J].电子测量与仪器学报,2022,36(6):113-125

改进卷积 Lenet-5 神经网络的轴承故障诊断方法

Improved convolutional Lenet-5 neural network for bearing fault diagnosis

DOI：

英文关键词:rolling bearing fault diagnosis complex working conditions Lenet-5 network network optimization

基金项目:甘肃省科技计划（21YF5GA072）、国家重点研发计划（2020YFB1713600）、甘肃省教育厅产业支撑计划（2021CYZC 02）项目资助

作者	单位
赵小强	1. 兰州理工大学电气工程与信息工程学院，2. 甘肃省工业过程先进控制重点实验室，3. 兰州理工大学国家级电气与控制工程实验室教学中心
罗维兰	1. 兰州理工大学电气工程与信息工程学院，2. 甘肃省工业过程先进控制重点实验室

Author	Institution
Zhao Xiaoqiang	1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology，2. Key Laboratory of Gansu Advanced Control for Industrial Processes,3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology
Luo Weilan	1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology，2. Key Laboratory of Gansu Advanced Control for Industrial Processes

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中文摘要:

针对滚动轴承微弱信号在强噪声、变工况复杂环境下,难以实现有效的故障诊断问题,提出了一种改进卷积 Lenet-5 神经网络的轴承故障诊断方法。首先,对采集的一维时域轴承振动信号进行预处理转化成便于卷积操作的二维灰度图;其次,将最基本的 Lenet-5 模型中的连续单向的传统卷积层改进为 Block1 模块、Block2 模块、Block3 模块,提取到更完整、更精准的特征信息;最后,为了防止网络出现过拟合现象,采用 L2 正则化和 Dropout 优化网络。为了验证本文所提方法在复杂工况环境的鲁棒和泛化性能,利用滚动轴承数据集和变速箱实验数据集进行实验验证。轴承数据集实验结果表明,本文所提出的方法在变噪声实验中准确率平均值都在 99. 3%;在变负荷实验中,故障诊断准确率都高于 90. 26%;在变工况实验中,故障诊断准确率平均值都高于 89. 01%;在变速箱数据集实验中,抗噪性故障诊断准确率高达 96. 3%。采用改进的 Lenet-5 方法对滚动轴承 12 种故障类型具有更好的分辨能力,在变工况下具有更好的抗干扰性和泛化性能。

英文摘要:

Aiming at the problem that it is difficult to realize effective fault diagnosis for weak signals of rolling bearings in the complex environment of strong noise and variable working conditions, a bearing fault diagnosis method based on improved convolutional Lenet-5 neural network is proposed. Firstly, the collected one-dimensional time-domain bearing vibration signals are preprocessed and converted into two-dimensional grayscale images which are convenient for convolution operation. Secondly, the continuous one-way traditional convolutional layers in the most basic Lenet-5 model are improved into Block1 module, Block2 module and Block3 module to extract more concrete and accurate feature information. Finally, L2 regularization and Dropout optimization are used to avoid overfitting. In order to verify the robustness and generalization performance of the proposed method in complex working conditions, experimental validation was carried out using the rolling bearing dataset and the gearbox experimental dataset. The experimental results of the bearing dataset show that the average accuracy of the proposed method in the variable noise experiments is 99. 3%. In the variable load experiments, the average accuracy of fault diagnosis is higher than 90. 26%. In the variable operating conditions experiments, the average accuracy of fault diagnosis is higher than 89. 01%. In the gearbox dataset experiments, the fault diagnosis accuracy of anti-noise is up to 96. 3%. The improved Lenet-5 method has the better ability of fault diagnosis for 12 fault types of rolling bearings, and has better anti-interference and generalization performance under variable working conditions.

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