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首页> 外文期刊>Mechanical systems and signal processing >Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis
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Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis

机译：通过改进的非局部方法和MEWT进行数据驱动的单组件特征识别，以进行机械传动系统故障诊断

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摘要

It is significant to perform condition monitoring and fault diagnosis on rolling mills in steel-making plant to ensure economic benefit. However, timely fault identification of key parts in a complicated industrial system under operating condition is still a challenging task since acquired condition signals are usually multi-modulated and inevitably mixed with strong noise. Therefore, a new data-driven mono-component identification method is proposed in this paper for diagnostic purpose. First, the modified nonlocal means algorithm (NLmeans) is proposed to reduce noise in vibration signals without destroying its original Fourier spectrum structure. During the modified NLmeans, two modifications are investigated and performed to improve denoising effect Then, the modified empirical wavelet transform (MEWT) is applied on the de-noised signal to adaptively extract empirical mono-component modes. Finally, the modes are analyzed for mechanical fault identification based on Hilbert transform. The results show that the proposed data-driven method owns superior performance during system operation compared with the MEWT method.
机译：对炼钢厂的轧机进行状态监测和故障诊断对确保经济效益具有重要意义。然而，在运行条件下，对复杂工业系统中的关键部件进行及时的故障识别仍然是一项艰巨的任务，因为获取的条件信号通常是多调制的，并且不可避免地会混入强烈的噪声。因此，本文提出了一种新的数据驱动的单组分识别方法以用于诊断。首先，提出了改进的非局部均值算法（NLmeans），以减少振动信号中的噪声而又不破坏其原始傅里叶频谱结构。在改进的NLmeans过程中，研究并进行了两次修改以提高去噪效果，然后，将改进的经验小波变换（MEWT）应用于去噪信号，以自适应地提取经验单分量模式。最后，对基于希尔伯特变换的机械故障识别模式进行了分析。结果表明，与MEWT方法相比，该数据驱动方法在系统运行过程中具有优越的性能。

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  • 来源
    《Mechanical systems and signal processing》 |2016年第12期|533-552|共20页
  • 作者

    Jun Pan; Jinglong Chen; Yanyang Zi; Jing Yuan; Binqiang Chen; Zhengjia He;

  • 作者单位

    State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China;

    Shanghai Institute of Radio Equipment, Shanghai 200090, PR China;

    Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China;

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  • 正文语种 eng
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  • 关键词

    Fault diagnosis; Nonlocal means; Data-driven Fourier spectrum segment; Empirical wavelet transform;

    机译：故障诊断;非本地手段;数据驱动的傅立叶频谱段;经验小波变换;

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