分数阶全息的转子起停车故障特征提取方法
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TH17

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国家自然科学基金资助项目(51365051,51421004);教育部新世纪优秀人才支持计划资助项目(NCET-13-0461);中央高校基本科研业务费专项资金资助项目


A Fault Feature Extraction Method for Rotor Start-Up or Slowdown Process Based on Fractional Fourier Transform and Holospectrum
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    摘要:

    针对传统起停车过程分析采用短时傅里叶变换提取瞬时幅值和相位会损失瞬变信息的不足,提出了基于分数阶全息原理的转子起停车故障特征提取方法。该方法利用分数阶傅里叶变换从转子起停车振动数据中提取随转速变化的各倍频分量,并通过Hilbert变换求取幅值和相位,克服了傅里叶变换的平均效应,保留了转子振动的瞬变信息。通过结合全息谱理论绘制分数阶全息瀑布图,提取出转子起停车状态下的故障特征。实验结果表明,该方法能够有效提取出起停车过程中振动信号的典型故障特征,对于常见的典型故障有很好的区分能力。

    Abstract:

    The vibration signal of the rotor start-up or slowdown process can be regarded as a dynamic response to a wide frequency range excitation, which contains more information than steady state vibration. As such, it deserves more attention to extracting fault features by the analysis of rotor start-up or slowdown process. In light of the defect that the analysis of conventional start-up or slowdown process applying STFT to obtain the amplitude and phase will lose transient information, a method based on fractional Fourier transform and holospectrum is proposed. In this method, amplitude and phase can be directly obtained from the complex envelop of each order component, so it can preserve the transient information and eliminate the average effect of STFT. In this paper, amplitudes and phases of the vibration from two perpendicular directions of a rotor section were obtained. A holo-watefall curve was plotted according to the holospectrum theory, then fault feature extraction was fulfilled. The experimental results show that the method can effectively extract the typical fault feature of the rotor and has strong ability to distinguish typical faults.

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  • 在线发布日期: 2016-09-05
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