...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis
【24h】

Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis

机译：自适应相关的Kurtogram及其在轮廓轴承系统故障诊断中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As one of the crucial subsystems, the performance of the wheelset-bearing system will significantly affect the running safety, stability and comfort of a high-speed train. Compared with the traditional rotating machinery vibration signals, those of wheelset-bearing systems have a distinct difference, for instance, the wheelset tread damage induced vibrations and bearing fault-related vibrations coexist and have similar wave-forms. Therefore, it is necessary to advance a reasonable method to identify the two kinds of cyclo-stationary components. To achieve this purpose, the adaptive correlated Kurtogram (ACK) is proposed. The proposed ACK method has two advantages: the first one is it can adaptively generate the paving of the plane for Kurtogram by using scale-space representation (SSR) to guarantee the detected boundaries cover the resonant frequency band well; the second one is it can highlight the special periodic impulses by correlated kurtosis. The scale-scale plane (SSP), which is obtained by SSR, is used to represent the frequency distribution characteristic of the acquired signal, firstly. After that, the paving of the plane is arbitrarily divided based on the SSP. Then the empirical wavelet transform (EWT) is employed to construct the sub-band signals according to the paving of the plane. The correlated kurtosis values with interesting periodic are calculated to distinguish the special repetitive transients. The nodes with the largest correlated kurtosis values are used to conduct a further envelope spectrum analysis. ACK method improves the effectiveness of the identification of the frequency bands containing impact information, and is capable of detecting multiple frequency bands simultaneously. The proposed ACK method is demonstrated by the simulated signal and two experimental signals acquired from a wheelset-bearing system test bench. The results indicate that the ACK can simultaneously extract the wheelset tread damage and bearing fault-related vibration component, and it is superior to the other traditional methods.
机译：作为一个关键的子系统之一，轮廓轴承系统的性能将显着影响高速列车的运行安全性，稳定性和舒适性。与传统的旋转机械振动信号相比，轮圈轴承系统具有不同的差异，例如，轮孔胎面损伤引起的振动和轴承故障相关的振动共存并具有相似的波形。因此，有必要推进合理的方法来识别两种环形固定组件。为达到此目的，提出了自适应相关的Kurtogram（ACK）。所提出的ACK方法具有两个优点：首先是通过使用尺度空间表示（SSR）来自适应地产生KurtoGram的平面铺设，以确保检测到的边界覆盖谐振频带很好;第二个是它可以通过相关的峰氏症突出特殊的周期性脉冲。通过SSR获得的刻度平面（SSP）用于表示所获取信号的频率分布特性。之后，基于SSP任意划分平面的铺路。然后，使用经验小波变换（EWT）来根据平面的铺设构造子带信号。计算具有有趣的周期性的相关峰值值以区分特殊的重复瞬变。具有最大相关峰值值的节点用于进行进一步的包络频谱分析。 ACK方法提高了包含影响信息的频带识别的有效性，并且能够同时检测多个频带。所提出的ACK方法通过模拟信号和从轮廓轴承系统测试台获取的两个实验信号来证明。结果表明，ACK可以同时提取轮孔胎面损坏和轴承故障相关的振动分量，并且它优于其他传统方法。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2021年第6期|107511.1-107511.21|共21页
  • 作者

    Zechao Liu; Shaopu Yang; Yongqiang Liu; Jianhui Lin; Xiaohui Gu;

  • 作者单位

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang 050043 China;

    State Key-Laboratory of Traction Power Southwest Jiaotong University Chengdu 610031 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang 050043 China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wheelset-bearing system; Scale-space representation; Kurtogram; Correlated kurtosis; Empirical wavelet transform;

    机译：轮廓轴承系统;规模空间表示;Kurtogram;相关的峰氏;经验小波变换;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号