[1]郝旺身,陈耀,孙浩,等.基于全矢-CNN的轴承故障诊断研究[J].郑州大学学报(工学版),2020,41(05):92-96.
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基于全矢-CNN的轴承故障诊断研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年05期
页码:
92-96
栏目:
出版日期:
2020-10-01

文章信息/Info

Title:
Bearing Fault Diagnosis Based on Full Vector-CNN
作者:
郝旺身陈耀孙浩付耀琨李伟
文献标志码:
A
摘要:
针对传统智能故障诊断系统需要大量先验知识,以及模型复杂度高和单通道信号不完整造成信息遺漏的问题,将全矢谱技术与卷积神经网络(CNN)结合,提出一种新的滚动轴承的故障诊断模型。该方法将全矢谱技术与深度卷积神经网络结合,相比于单通道数据建立的模型而言,具有特征信息完整、模型适应性强等优点。首先利用全矢谱技术对采集的双通道信号进行信息融合,得到融合后的主振矢数据。然后结合主振矢数据与深度学习算法构建全矢深度卷积神经网络,模型能够自适应地提取故障特征,利用反向传播算法调节优化模型参数。实验结果表明:该方法能够提取更加完整的轴承故障信息,该模型具有更高的准确率和更好的稳定性。
Abstract:
Aimed to improved at the traditional intelligent fault diagnosis system, which required a largeamount of prior knowledge, and had the complexity of its model and the information loss caused by the incom-pleteness of single-channel signal, a full-vector deep convolutional neural network diagnosis model of rollingbearing was proposed. The full-vector technique was used to fuse the acquired two-channel signals to obtain thefused main vibration vector data, which contained more complete information than the single-channel data.Combining the main vibrationvector and deep learning algorithm to construct the full vector depthconvolutional neural network, the model could adaptively extract the fault features and use the back propaga-tion algorithm to adjust the model parameters. The experimental results showed that the method could extractmore complete fault information, and the model had higher accuracy and better stability.
更新日期/Last Update: 2020-10-23