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Gear Fault Diagnosis Under the Run-Up Condition Using Fractional Fourier Transform and Hilbert Transform

机译:使用分数傅里叶变换和Hilbert变换的齿轮故障诊断

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The sidebands spaced around the gear meshing content and its harmonics are the commonly used fault indicator in the gear fault diagnosis under the constant rotational speed condition. However, when the gear works under the run-up condition, the variable rotational speed causes smearing to the frequency spectrum, which makes it difficult to recognize the sidebands caused by the local gear fault. This paper proposed a method which combines Fractional Fourier Transform (FrFT) and the Hilbert Transform (HT) to identify the sidebands of signal measured under the run-up process. The HT is utilized to construct the analytic representation of the measured signal, which has a better energy concentration than the measured signal in the fractional domain. Thus, the ability of extracting weak sidebands of FrFT is enhanced. Simulation case study and experimental case study are carried to verify the effectiveness of the proposed method. Tooth cracks of different depth are manufactured artificially to simulate the local fault of different severity. The results show that the weak sidebands which is invisible in the time-frequency representation can be identified by the proposed method. The amplitude of gear meshing content and its sidebands ascends with the growth of the crack depth.
机译:边带间隔围绕齿轮啮合内容及其谐波是在恒定转速条件下,齿轮故障诊断常用的故障指示器。然而,当齿轮助跑条件下工作,可变转速使涂抹到频谱,这使得难以识别所造成的局部齿轮故障的边带。本文提出了一种结合了分数傅里叶变换(分数傅里叶变换)的方法和希尔伯特变换(HT),以确定信号的下助跑过程测得的边带。所述HT是用于建造所述测量的信号,其具有比在分数域所测量的信号更好的能量浓度的解析表示。因此,提取分数傅里叶变换的弱边带的能力增强。模拟案例研究和实验案例研究被执行以验证所提出的方法的有效性。不同深度的齿裂缝被人为地制造以模拟不同程度的局部故障。结果表明,弱边带这是在时频表示不可见可以通过所提出的方法来鉴定。齿轮啮合内容及其边带的上升与所述裂纹深度的生长幅度。

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