Abstract: Due to influences of noise and complex transmission paths， the wind turbine gear fault signatures were generally weak. To effectively diagnose the gear faults， a new blind deconvolution method  was proposed based on reweighted-kurtosis maximization. The reweighted-kurtosis possessed great robustness to single or few strong impulse interferences and did not require any prior knowledge of the fault impulse train to be restored. The proposed deconvolution method may effectively solve the problems that the classical kurtosis maximization-based methods tend to restore a single dominant impulse rather than the gear fault impulse train. At the same time， the proposed method has stronger applicability in gear fault diagnosis for industrial equipment in comparison with the common non-fully “blind” methods（relying on the prior knowledge of the fault characteristic frequency）. The analysis results of the simulated signals show that the proposed method is effective in restoring fault impulse trains. The applications in wind turbine fault diagnosis demonstrate the effectiveness of the proposed method for gear fault diagnosis. 

Key words: wind turbine, gear, fault diagnosis, reweighted-kurtosis, blind deconvolution

摘要： 受噪声以及复杂传递路径等影响，风电机组齿轮故障特征信号通常比较微弱。为有效诊断齿轮故障，提出一种新的盲解卷积方法——最大重加权峭度盲解卷积方法。重加权峭度对故障信号中单个或少量强冲击干扰具有很好的鲁棒性，且无需待恢复故障冲击序列先验知识。最大重加权峭度盲解卷积方法能有效地解决经典的基于峭度最大化方法倾向于恢复单个主导冲击而非齿轮故障冲击序列的问题，同时相较于常见非全“盲”（依赖故障特征频率先验）方法在工业装备齿轮故障诊断方面具有更强的适用性。仿真信号分析结果表明所提方法在恢复故障冲击序列方面效果显著，在风电机组故障诊断中的应用案例证实了所提方法对齿轮故障诊断的有效性。

关键词: 风电机组, 齿轮, 故障诊断, 重加权峭度, 盲解卷积