【摘要】 针对强噪声背景下信噪比较低的旋转机械故障诊断问题,提出一种基于解析模态分解(AMD)和随机共振的旋转机械故障诊断方法。若信号的频率成分已知,AMD方法能将多频率成分的信号分解为单频率信号。对于可预知故障特征频率的旋转机械故障诊断,首先利用AMD方法提取振动信号中故障特征频率所在频段的信号,并对每个提取出的信号添加强度较低的噪声;然后利用粒子群算法优化的双稳随机共振对含噪信号进行处理来加强信号;最后求该信号的频谱,若频谱中含有故障特征频率,则说明振动信号中存在该故障。通过对滚动轴承故障信号特征的提取证明了该方法有良好的效果。更多还原

【Abstract】 Aiming at the problem of fault diagnosis for rotating machinery with very low signal-to-noise ratio under strong noise background,a fault diagnosis method for rotating machinery based on AMD and stochastic resonance is proposed.If the frequency components of the signal are known,signal with different frequency components can be decomposed into single frequency signal using the AMD method,especially to decompose a signal with closely spaced frequency components.For the fault feature frequency can be predicted in rotating machinery fault diagnosis,AMD method is used to extract fault feature frequency signal in mechanical vibration signal and add low intensity noise to the signal firstly.Then,the signal with noise is put into the optimal stochastic resonance system and denoising and enhancing the signal.Finally the spectrum of the signal is obtained,if the frequency spectrum contains the fault feature frequency,it shows that the faults exist in mechanical vibration signal.Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.更多还原