基于时延降噪循环双谱的电机轴承故障检测方法研究

当电机轴承出现故障时，定子电流信号中的微弱故障特征淹没在基波及谐波的强大噪声背景中。由于信噪比过低，基于电流的轴承故障检测一直是一个挑战。为此，提出了一种基于时延降噪的循环双谱检测方法。首先根据循环双谱函数构造定子电流解析表达式，提取电流解析式的特征解。通过理论分析确定循环双谱函数中的时延滞后量，减少电流信号中的无关噪声成分。最后利用实验平台采集定子电流，在电流解析式取得特征解的位置做切片频谱分析。实验结果表明，所提方法能有效提升信噪比，并提取出电流中非平稳的微弱故障特征，实现电机轴承故障的检测。

Research on the detection method of motor bearing faults based on time shift and cyclic bispectrum

When motor bearing faults occur, the weak fault features in the stator current signal are drowned in the strong noise background of fundamental waves and harmonics. Because of the low signal-to-noise ratio, current-based bearing fault detection has always been a challenge. To this end, this paper proposes a detection method based on time shift denoising and a cyclic bispectrum. First, the stator current analytical expression is constructed according to the cyclic bispectrum function, and the characteristic solution of the current analytical expression is extracted. Then, the unrelated noise component in the stator current signal is decomposed, and the delayin the cyclic bispectrum function is quantitatively analyzed, and the optimal time shift is determined. Finally, the stator current is collected by the experimental platform, and slice spectrum analysis is performed at the position where the current solution is used to obtain the characteristic solution. The experimental results show that the proposed method can effectively improve the signal-to-noise ratio and extract non-stationary weak fault features in the current to realize the detection of motor bearing faults. This work is supported by National Natural Science Foundation of China (No. 51279020).