在基于振动监测的齿轮箱故障诊断中，如何准确提取由缺陷引起的周期性瞬态冲击信号是实现故障诊断的关键。但实际测量的振动信号往往包含多种干扰成分，使得瞬态冲击的提取十分困难。对此，本文提出一种基于谐波特征的稀疏增强正则化方法。该方法首先建立关于齿轮箱故障信号的加权稀疏优化模型；其次，基于谐波特征构造了一种反映周期性瞬态冲击信号强弱的指标；最后基于该指标实施重加权正则化，从而对干扰信号施加惩罚以增强降噪性能。对仿真信号和实际案例的分析结果验证了所提出方法对故障信号的重建精度优于其他稀疏分解方法和常规信号处理方法，因此能够提供更加准确的齿轮箱故障诊断结果。

In vibration-based gearbox fault diagnosis, accurately extracting periodic transient impact signals caused by defects is critical for achieving fault diagnosis. However, the vibration signals measured in practice often contain various interference components, making the extraction of transient impacts quite challenging. To address the issue, this paper proposes a sparsity-enhancing regularization method based on harmonic features. Firstly, a weighted sparse optimization model for gearbox fault signals is established. Secondly, an indicator reflecting the strength of periodic transient impact signals is constructed based on harmonic features. Finally, reweighted regularization is implemented based on the indicator to penalize interference signals, thus enhancing the noise reduction capability. The analysis results of both simulated signals and practical cases verify that the proposed method outperforms other sparse decomposition methods and conventional signal processing methods in terms of the reconstruction accuracy for fault signals, thereby providing more accurate gearbox fault diagnosis results.