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Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization

机译:使用Hilbert-Huang变换和瞬时无量纲频率归一化来表征变速齿轮故障

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The objective of this research is to investigate the feasibility of utilizing the instantaneous dimensionless frequency (DLF) normalization and Hilbert-Huang Transform (HHT) to characterize the different gear faults in case of variable rotating speed. The normalized DLF of the vibration signals are calculated based on the rotating speed of shaft and the instantaneous frequencies of Intrinsic Mode Functions (IMFs) which are decomposed by Empirical Mode Decomposition (EMD) process. The faulty gear features on DLF-energy distribution of vibration signal can be extracted without the presence of shaft rotating speed, so that the proposed approach can be applied for characterizing the malfunctions of gearbox system under variable shaft rotating speed. A test rig of gear transmission system is performed to illustrate the gear faults, including worn tooth, broken tooth and gear unbalance. Different methods to determine the instantaneous frequency are employed to verify the consistence of characterization results. The DLF-energy distributions of vibration signals are investigated in different faulty gear conditions. The analysis results demonstrate the capability and effectiveness of the proposed approach for characterizing the gear malfunctions at the DLFs corresponding to the meshing frequency as well as the shaft rotating frequency. The support vector machine (SVM) is then employed to classify the vibration patterns of gear transmission system at different malfunctions. Using the energy distribution at the characteristic DLFs as the features, the different fault types of gear can be identified by SVM with high accuracy.
机译:这项研究的目的是研究利用瞬时无量纲频率(DLF)归一化和Hilbert-Huang变换(HHT)来表征在变速情况下不同齿轮故障的可行性。振动信号的归一化DLF是根据轴的旋转速度和通过经验模式分解(EMD)过程分解的本征模式函数(IMF)的瞬时频率计算的。可以在不存在轴转速的情况下提取振动信号的DLF能量分布上的故障齿轮特征,从而将所提出的方法应用于表征变速箱系统在轴转速可变的情况下的故障。进行齿轮传动系统的试验台以说明齿轮故障,包括磨损的齿,断齿和齿轮不平衡。确定瞬时频率的不同方法被用来验证表征结果的一致性。研究了不同故障档位条件下振动信号的DLF能量分布。分析结果证明了所提出的方法在表征与啮合频率以及轴旋转频率相对应的DLF处齿轮故障方面的能力和有效性。然后,使用支持向量机(SVM)对齿轮传动系统在不同故障时的振动模式进行分类。利用特征DLF处的能量分布作为特征,可以通过SVM高精度地识别齿轮的不同故障类型。

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