基于Teager-Huang变换的齿轮磨损故障检测与诊断

Hui Li, Lihui Fu, Zhentao Li
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引用次数: 3

摘要

提出了一种基于Teager-Huang变换的齿轮磨损故障诊断方法。该方法基于经验模态分解(EMD)和Teager Kaiser能量算子(TKEO)技术。EMD可以自适应地将振动信号分解为一系列均值为零的调幅-调频(AM-FM)本征模态函数(IMFs)。TKEO可以在任意时刻跟踪AM-FM分量的瞬时幅度和瞬时频率。通过实例验证了该方法的有效性。实验结果有力地证明了Teager-Huang变换方法在齿轮故障检测中的性能优于Hilbert-Huang变换方法。Teager-Huang变换能有效诊断齿轮磨损故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Detection and Diagnosis of Gear Wear Based on Teager-Huang Transform
A new approach to fault diagnosis of gear wear based on Teager-Huang transform is presented. This method is based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM)Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for gear fault detection. Teager-Huang transform can effectively diagnose the faults of the gear wear.
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