摘要
针对电机轴承早期故障状态识别困难这一问题，提出了最大相关峭度反褶积和Teager能量算子相结合的诊断方法。首先利用最大相关峭度反褶积算法对原始信号进行降噪预处理，削弱冗余噪声成分的干扰，增强故障特征，继而计算降噪信号的Teager能量算子输出，并对所得的瞬时能量信号做频谱分析，最终通过分析能量谱中幅值突出的频率成分判别轴承的状态。实测信号分析结果表明，将最大相关峭度反褶积和Teager能量算子二者相互结合，能够有效提取电机轴承信号中的微弱故障特征，从而实现早期故障的精确诊断。
To overcome the difficulty of condition judgment for motor bearings with incipient fault, a diagnosis method based on maximum correlated kurtosis deconvolution and Teager energy operator was proposed. Firstly, the original fault signal was prcprocessed by the maximum correlated kurtosis deconvolution algorithm, the interference of the redundant noise component could be weakened and the fault characteristic could be more obvious. Then the output of the Teager energy operator of the denoised signal was calculated, and the frequency spectrum analysis was performed on the obtained instantaneous energy signal. Finally, the condition of the motor bearing could be judged by analyzing the frequency components with prominent amplitude in the energy spectrum. The analysis results of the measured signal showed that, the weak fault feature of the signals of the motor bearings could be effectively extracted when the maximum correlated kurtosis deconvolution and Teager energy operator methods are combined, then the accurate diagnosis of incipient fault could be realized.
出处
《现代科学仪器》
2016年第4期120-126,共7页
Modern Scientific Instruments
