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Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis

机译:通过故障特征阶数(FCO)分析诊断滚动轴承故障

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摘要

Order tracking based on time-frequency representation (TFR) is one of the most effective methods for gear fault detection under time-varying rotational speed without using a tachometer. However, for a rolling element bearing, the signal components related to rotational speed usually cannot be directly extracted from the TFR. As such, we propose a new method to solve this problem. This method consists of four main steps: (a) signal filtering via fast spectral kurtosis (SK) analysis - this together with the short time Fourier transform (STFT) leads to a TFR of the filtered signal with clear fault-revealing trend lines, (b) extraction of instantaneous fault characteristic frequency (IFCF) from the TFR using an amplitude-sum based spectral peak search algorithm, (c) signal resampling based on the extracted IFCF to convert the non-stationary time-domain signal into the stationary fault phase angle (FPA) domain signal, and (d) transform of the FPA domain signal into the domain of the fault characteristic order (FCO) and identification of fault type from the FCO spectrum. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.
机译:在不使用转速计的情况下,基于时频表示(TFR)的订单跟踪是在时变转速下进行齿轮故障检测的最有效方法之一。但是,对于滚动轴承,通常不能直接从TFR中提取与转速有关的信号分量。因此,我们提出了一种解决该问题的新方法。该方法包括四个主要步骤:(a)通过快速频谱峰度(SK)分析进行信号滤波-结合短时傅立叶变换(STFT)导致具有清晰的故障揭示趋势线的滤波信号的TFR,( b)使用基于振幅和的频谱峰值搜索算法从TFR中提取瞬时故障特征频率(IFCF),(c)基于提取的IFCF进行信号重采样,以将非平稳时域信号转换为固定故障相角(FPA)域信号,以及(d)将FPA域信号转换为故障特征阶数(FCO)的域,并从FCO频谱中识别故障类型。仿真和实验轴承振动信号均验证了该方法的有效性。

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