Structural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox.

中文翻译：

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基于瞬时频率估计的变速模式齿轮故障检测在真实速度下的应用

具有多个啮合点的结构紧凑性激发了以不同速度运行的风力涡轮机行星齿轮箱中的多种振动。非平稳的多分量振动信号会导致复杂的调制，从而挑战了基于信号处理技术的故障诊断方法的有效性。本文旨在基于分解分量的瞬时频率（IF）估计来检测变速条件下的齿轮故障。振动信号使用变分模式分解（VMD）进行分解，该模型能够通过表现出单振荡成分来进行频率解调，然后通过IF估计来选择最敏感的分解模式。进一步分析了所选的敏感模式，以推断出包络频谱，以提取突出故障症状的频率分量。此外，对置换熵（PE）进行了评估，以表征由于故障严重性而导致的随机性的存在，并验证所提出的信号处理方法。在本文中，使用提出的信号处理方法分析了模拟行星齿轮故障的振动信号。通过在实时变速下进行的实验研究，分析了行星齿轮故障的症状，以验证并展示所提出的行星齿轮箱故障诊断方法的有效性。在本文中，使用提出的信号处理方法分析了模拟行星齿轮故障的振动信号。通过在实时变速下进行的实验研究，分析了行星齿轮故障的症状，以验证并展示所提出的行星齿轮箱故障诊断方法的有效性。在本文中，使用提出的信号处理方法分析了模拟行星齿轮故障的振动信号。通过在实时变速下进行的实验研究，分析了行星齿轮故障的症状，以验证并展示所提出的行星齿轮箱故障诊断方法的有效性。