摘要 在故障诊断领域,电机电流信号分析法(MCSA)已经逐渐应用于齿轮故障诊断中,但该方法在诊断行星轮缺齿故障时由于电流基频干扰较大,导致故障特征不明显,难以实现故障诊断。因此提出一种基于电流信号经验模态分解(EMD)的故障诊断方法。通过对电机电流信号进行EMD分解,选取合适的IMF分量经傅立叶变换求其频谱图,根据频谱图中是否存在与故障特征频率相关的频率,实现了对行星轮缺齿故障的有效诊断。并通过实验分析,验证了该方法的有效性。 In the field of fault diagnosis,motor current signal analysis(MCSA)has been gradually applied to gear fault diagnosis,but the fault features are not obvious due to the large current base frequency interference in the diagnosis of planetary gear tooth fault.Therefore,this paper proposes a fault diagnosis method based on the empirical mode decomposition(EMD)of current signals.Through EMD decomposition of the motor current signal,the appropriate IMF component is selected through Fourier transform to obtain its spectrum diagram.According to whether there are frequencies related to the fault characteristic frequency in the spectrum diagram,the effective diagnosis of the fault of planetary gear tooth is realized.The effectiveness of the method is verified by experimental analysis.
机构地区 太原理工大学机械与运载工程学院 阳煤集团
出处 《机械设计与制造》 北大核心 2021年第4期39-42,47,共5页 Machinery Design & Manufacture
基金 基于振动与电机电流信息融合的转子系统载荷识别及故障诊断方法(51475318)。
关键词 故障诊断 行星轮 经验模态分解(EMD) 电机电流 Fault Diagnosis Planetary Wheel Empirical Mode Decomposition(EMD) Motor Current
