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Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

机译:改进的最大相关峰度反褶积方法在滚动轴承故障诊断中的应用

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摘要

The extraction of periodic impulses, which are the important indicators of rolling bearing faults, from vibration signals is considerably significance for fault diagnosis. Maximum correlated kurtosis deconvolution (MCKD) developed from minimum entropy deconvolution (MED) has been proven as an efficient tool for enhancing the periodic impulses in the diagnosis of rolling element bearings and gearboxes. However, challenges still exist when MCKD is applied to the bearings operating under harsh working conditions. The difficulties mainly come from the rigorous requires for the multi-input parameters and the complicated resampling process. To overcome these limitations, an improved MCKD (IMCKD) is presented in this paper. The new method estimates the iterative period by calculating the autocorrelation of the envelope signal rather than relies on the provided prior period. Moreover, the iterative period will gradually approach to the true fault period through updating the iterative period after every iterative step. Since IMCKD is unaffected by the impulse signals with the high kurtosis value, the new method selects the maximum kurtosis filtered signal as the final choice from all candidates in the assigned iterative counts. Compared with MCKD, IMCKD has three advantages. First, without considering prior period and the choice of the order of shift, IMCKD is more efficient and has higher robustness. Second, the resampling process is not necessary for IMCKD, which is greatly convenient for the subsequent frequency spectrum analysis and envelope spectrum analysis without resetting the sampling rate. Third, IMCKD has a significant performance advantage in diagnosing the bearing compound-fault which expands the application range. Finally, the effectiveness and superiority of IMCKD are validated by a number of simulated bearing fault signals and applying to compound faults and single fault diagnosis of a locomotive bearing.
机译:从振动信号中提取周期性脉冲是滚动轴承故障的重要指标,对故障诊断具有重要意义。由最小熵反褶积(MED)开发的最大相关峰度反褶积(MCKD)已被证明是一种用于增强滚动轴承和齿轮箱诊断中的周期性脉冲的有效工具。但是,将MCKD应用于在恶劣工作条件下运行的轴承时,仍然存在挑战。困难主要来自对多输入参数的严格要求和复杂的重采样过程。为了克服这些限制,本文提出了一种改进的MCKD(IMCKD)。新方法通过计算包络信号的自相关来估计迭代周期,而不是依赖于所提供的先前周期。而且,在每个迭代步骤之后,通过更新迭代周期,迭代周期将逐渐接近真正的故障周期。由于IMCKD不受峰度值高的脉冲信号的影响,因此该新方法从分配的迭代计数中的所有候选项中选择最大峰度滤波信号作为最终选择。与MCKD相比,IMCKD具有三个优点。首先,IMCKD在不考虑先前时间和轮换顺序的选择的情况下,效率更高,并且具有更高的鲁棒性。其次,IMCKD不需要重采样过程,这对于随后的频谱分析和包络频谱分析非常方便,而无需重置采样率。第三,IMCKD在诊断轴承复合故障方面具有显着的性能优势,从而扩大了应用范围。最后,通过大量的模拟轴承故障信号验证了IMCKD的有效性和优越性,并将其应用于机车轴承的复合故障和单故障诊断中。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2017年第8期|173-195|共23页
  • 作者单位

    Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an710049, China;

    Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an710049, China,Center for Intelligent Maintenance Systems, University of Cincinnati, OH 45221, USA;

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710054, China;

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710054, China;

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

    Maximum correlated kurtosis; deconvolution (MCKD); Improved MCKD; Rolling element bearing; Fault diagnosis; Compound-fault; Autocorrelation;

    机译:最大相关峰度;去卷积(MCKD);改进的MCKD;滚动轴承;故障诊断;复合故障自相关;

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