受基频频谱泄露影响,经典MCSA方法诊断鼠笼电机转子断条故障时的诊断能力严重依赖于电机负载大小。针对这一问题,提出了基于定子电流信号平方解调制分析诊断方法。首先采用硬件方式对定子电流信号作基于平方解调制的信号预处理,以此消除制约诊断能力的基频频谱泄露,继而对解调后的信号作快速傅里叶变换,然后根据频谱中是否存在特征频率成分判断转子断条故障发生与否。在3 k W电机实验平台上对所提出的方法进行实验验证。实验结果表明,即使鼠笼电机在轻载或空载条件下运行时所提出的方法仍然能够诊断出转子断条故障,从而有效提高了诊断能力。
When used in the broken rotor bar fault detection of squirrel cage motor, the classical motor current signature analysis (MCSA) method has the drawback that its diagnosis capacity depends strongly on the loads of the motor because the spectral leakage has a special adverse impact on the characteristic sideband frequencies of the fault. Aiming at this problem, a diagnosis method based on square stator current demodulation analysis is proposed. Firstly, the stator current signal is preprocessed with hardware circuit method based on square stator current demodulation to eliminate the fundamental spectrum leakage that restricts the diagnosis capacity. Then, the fast Fourier transform is conducted on the demodulated signal. According to the fact that if the characteristic frequency exists in the spectrum, whether the broken rotor bar fault occurs is determined. The experiment verification was performed on a 3 kw motor experiment test bench. The experiment results show that the presented method could detect the broken rotor bar fault even if the machine operates under low load or no-load conditions, and the fault diagnosis ability is improved.