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Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

机译：基于实际速度分解和实际速度场景下的瞬时频率估计的齿轮故障检测

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摘要

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.
机译：具有多个啮合点的结构紧凑性激发在以不同速度操作的风力涡轮机的行星齿轮箱中的多个振动。非营养的多组分振动信号导致复杂的调制，具点挑战信号处理技术的故障诊断方法的有效性。本文旨在基于分解组分的瞬时频率（IF）估计来检测不同速度条件下的齿轮齿故障。使用变分模式分解（VMD）分解振动信号，该分解模块能够通过展示单振荡组件来频率解调，然后是IF估计来选择最敏感的分解模式。进一步分析所选的敏感模式以推断出频率分量提取的包络谱突出显示故障症状。此外，评估置换熵（PE）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。本文使用所提出的信号处理方法分析模拟行星齿轮故障的振动信号。使用实际不同速度的实验研究分析了行星牙齿故障的症状，以验证并表现出行星齿轮箱的拟议故障诊断方法的有效性。

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