基于多维特征评价的风机齿轮箱早期故障诊断
CSTR:
作者:
作者单位:

浙江工业大学 信息工程学院,杭州 310014

作者简介:

通讯作者:

E-mail: xiangwu@zjut.edu.cn.

中图分类号:

TM315

基金项目:


Incipient fault diagnosis for wind turbine gearbox based on multidimensional feature evaluation
Author:
Affiliation:

College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为了及时有效地诊断风机齿轮箱早期微弱故障,针对齿轮箱微弱故障信号非线性、非平稳、低幅值、低信噪比的特点,提出一种基于多维特征评价的风机齿轮箱早期故障诊断方法.首先,利用变分模态分解将原始振动信号分解为多个固有模态分量,并构建“信息熵-峭度-包络谱峭度”多维特征评价模型,结合熵权法筛选关键特征分量以重构信号;其次,运用改进的小波阈值法降低噪声干扰对重构信号的影响,得到显著的故障冲击特征;再者,使用宽度学习系统进行状态识别,并利用$L_{21

    Abstract:

    In order to timely and effectively diagnose the incipient weak faults of a wind turbine gearbox, a fault diagnosis method for a wind turbine gearbox is proposed, which deals with the nonlinear, nonstationary, low amplitude and low SNR vibration signals. Firstly, the original vibration signal is decomposed into multiple intrinsic mode functions by using the optimal variational mode decomposition. Meanwhile, an “information entropy-kurtosis-envelop spectrum kurtosis” multidimensional feature evaluation model is constructed, which is combined with the entropy weight method to screen key intrinsic mode functions to reconstruct the signal. Then an improved wavelet threshold method is designed to perform secondary noise reduction, and the obvious fault shock characteristics are obtained. The broad learning system is used for fault classification, and the $L_{21

    参考文献
    相似文献
    引证文献
引用本文

郭方洪,林凯,窦云飞,等.基于多维特征评价的风机齿轮箱早期故障诊断[J].控制与决策,2024,39(5):1566-1576

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-04-17
  • 出版日期: 2024-05-20
文章二维码