基于参数优化MOMEDA和Teager能量算子的行星轮故障诊断

Planetary Gear Fault Diagnosis Based on Parameter Optimization of MOMEDA and TEAGER Energy Operator

摘要: 针对强背景噪声环境下行星系统行星轮故障特征信息微弱，鉴于Teager能量算子在故障特征提取方面的局限性，本文提出了一种结合参数优化的多点最优最小熵解卷积(MOMEDA)与Teager能量算子的行星轮点蚀故障特征提取新方法。该方法首先对故障信号实施MOMEDA解卷积处理，随后通过分析多点峭度谱图来辨识故障信号中的周期性成分。采用Teager能量算子增强故障冲击特征，削弱干扰信号，进而得到Teager能量谱。对比能量谱图与理论计算的故障特征频率，识别行星轮故障特征。在实际AT变速器关键部件故障信号特征提取台架试验中，将该方法应用于多排行星轮系统齿轮点蚀故障中进行验证，试验结果证明能实现行星轮故障特征的准确提取。

Abstract: In the context of weak fault feature information of planetary gears in planetary systems under strong background noise, Addressing the constraints of the Teager energy operator in fault feature extraction, this paper introduces an innovative approach that integrates Multi-point Optimal Minimum Entropy Deconvolution (MOMEDA) with parameter tuning and the Teager energy operator to extract pitting fault features in planetary gears. This method first performs MOMEDA deconvolution processing on the fault signal, Subsequently, periodic components within the fault signal are identified through the analysis of the multi-point kurtosis spectrum. The Teager energy operator is used to enhance the fault impact features and weaken the interference signals, thereby obtaining the Teager energy spectrum. By comparing the energy spectrum with the theoretically calculated fault feature frequencies, the fault features of the planetary gears can be identified. In the actual bench test for extracting the fault signal features of key components of the AT transmission, this method was applied to the pitting fault of multi-row planetary gear systems for verification. The test results prove that the fault features of the planetary gears can be accurately extracted.