Abstract Planetary subassemblies in wind turbine gearbox are subject to compound faults due to harsh environment and complex structure. Disturbed by the meshing vibration from higher-speed transmission stage and intensive noise, fault diagnosis of planetary subassemblies with low rotational speed is challenging. In this paper, a tunable Q-factor wavelet transform based sparse representation method is proposed, which integrates the property of tunable Q wavelet transform, non-convex penalty and noise optimization into sparse decomposition. This method makes it possible to accurately decompose vibration signal from faulty planetary subassemblies into two resonance components and noise, relying less on the setting of regularization parameters due to the noise restriction. It is easier to detect potential fault information in decomposed low or high resonance component than in the original signal. Further, normalized multi-stage enveloping spectrogram is presented to reveal the fault characteristic frequencies of planetary gears and bearings even though they are weak. The effectiveness of the proposed methods is verified by the analysis of a simulated faulty signal and an on-site case from one 850 kW wind turbine gearbox.

中文翻译：

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使用基于 TQWT 的稀疏表示法检测风力涡轮机齿轮箱中的行星组件故障

摘要 风力发电机齿轮箱中的行星组件由于环境恶劣和结构复杂而容易发生复合故障。受高速传动级的啮合振动和强烈噪声的干扰，低转速行星组件的故障诊断具有挑战性。本文提出了一种基于可调Q因子小波变换的稀疏表示方法，将可调Q小波变换的特性、非凸惩罚和噪声优化融合到稀疏分解中。这种方法可以将来自故障行星组件的振动信号准确地分解为两个共振分量和噪声，由于噪声限制而较少依赖正则化参数的设置。在分解的低或高谐振分量中比在原始信号中更容易检测潜在故障信息。此外，还提出了归一化的多级包络频谱图，以揭示行星齿轮和轴承即使很弱的故障特征频率。通过对来自一台 850 kW 风力涡轮机齿轮箱的模拟故障信号和现场案例的分析，验证了所提出方法的有效性。