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首页 > 过刊浏览>2024年第44卷第5期 >1017-1022. DOI:10.16450/j.cnki.issn.1004-6801.2024.05.026
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基于CNN与BLS的滚动轴承故障诊断方法
作者:
作者单位:

1.青岛理工大学机械与汽车工程学院 青岛,266520;2.青岛大学计算机科学技术学院 青岛,266071

中图分类号:

TH17

基金项目:

山东省自然科学基金资助项目（ZR2019PEE018， ZR2020QE158）；山东省科技型中小企业创新能力提升资助项目（2021TSGC1063）；青岛市自然科学基金资助项目（23-2-1-216-zyyd-jch）

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    摘要:

    针对传统滚动轴承故障诊断方法训练时间长和效率低的问题，提出一种基于卷积神经网络（convolutional neural networks，简称CNN）和宽度学习系统（broad learning system，简称BLS）的故障诊断方法，实现了端到端的快速准确模式识别。首先，建立CNN与BLS结合的宽度卷积学习系统（broad convolutional learning system，简称BCLS），利用CNN提取信号特征和BLS进行分类，获得系统输出；其次，通过残差学习增加BLS层数，形成堆叠宽度卷积学习系统（stacked broad convolutional learning system，简称SBCLS），优化预测输出与真实标签的误差，对轴承故障模式进行识别；最后，通过试验将所提方法与3种BLS方法的预测结果进行了比较验证。结果表明，与几种常见故障诊断方法相比，所提方法诊断效果更佳，具有更高的准确率和训练效率，在边缘端的智能故障诊断中具有较好的应用前景。

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历史
  • 收稿日期:2022-04-29
  • 最后修改日期:2022-09-06
  • 在线发布日期: 2024-10-15
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