基于低次故障特征阶比系数的变转速滚动轴承等效转频估计算法
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摘要
基于时频表达的阶比跟踪算法能够帮助实现变转速旋转设备的故障诊断而不依靠转速计的帮助。然而,滚动轴承振动信号的时频表达却因缺少明显且易提取的转频成分使得该算法无法得到应用。为此,引入瞬时故障特征频率作为滚动轴承的等效转频,并提出基于低次故障特征阶比系数的估计算法对其进行提取。根据转频的变化范围确定基本滤波带宽并沿频率轴构造一组带通滤波器对原信号进行带通滤波。计算各带通滤波结果的包络时频谱并提取相应的等效转频趋势线。利用等效转频趋势线对相应的滤波结果进行重采样并计算故障特征阶比包络谱。将各阶比包络谱中与一、二、三次故障特征阶比相对应的幅值之和作为标准确定最优滤波带宽与相应的等效转频趋势线(瞬时故障特征频率趋势线)。利用该趋势线对原始信号进行故障相角域重采样,所得结果故障特征阶比包络谱即可用于滚动轴承的故障诊断。仿真算例和应用实例证明了该算法的有效性。
Extraction of the rotational frequency is the key step of employing order tracking based on the time-frequency representation(TFR) in fault diagnosis of rotational mechanical machinery under varying rotational speed the assistance of a speed sensor. However, the lack of obvious and extractable rotational frequency components in the TFR of the vibration signal of the rolling bearing will block the usage of this algorithm. As such, a new equivalent rotational frequency(ERF) of IRF, the instantaneous fault characteristic frequency(IFCF), is introduced, and a corresponding estimation algorithm based on the lower fault characteristic order coefficient is proposed to estimate this ERF. A series of band-pass filters whose frequency bands can be determined by the range of rotational frequency are constructed along the frequency axis. The ERFs of all these band-pass filtered results are then extracted from the envelope time-frequency representation of the corresponding band-pass filtered results. The fault characteristic order(FCO) envelope spectrums are calculated based on the resampled version of the band-passed filtered signal using the corresponding ERF. The lower fault characteristic order coefficient which equals to the amplitude summation of the 1st, the 2nd and the 3rd fault characteristic order is finally used to determine the best filtered band and corresponding ERF. Resampling the original vibration signal with the IFCF trend can result in the resampled signal which can be used for the final diagnosis of rolling bearing under the varying rotational speed. The effectiveness of the proposed method is validated by both simulated and experimental bearing vibration signals.
引文
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