基于频域稀疏分类的滚动轴承故障诊断方法

摘要

For the problem that the fault diagnosis methods based on feature frequency identification for rolling bearings are susceptible to strong noise,a diagnosis method based on frequency-domain sparse classification is proposed.First-ly,the vibration signals of bearings with known fault types are transformed from time to frequency domain,and the fre-quency domain transform coefficients are used to construct a training dictionary.Then the frequency domain coefficients of vibration signals of test bearings are sparsely decomposed on dictionary to get sparse coefficients.The fault types are determined according to minimum reconstruction error.The test results demonstrate that the method can effectively over-come noise interference and avoid estimation of fault feature frequency.%针对基于特征频率识别的滚动轴承故障诊断方法存在易受强噪声干扰的问题，提出基于频域稀疏分类算法的诊断方法。首先对已知故障类型的滚动轴承振动信号进行时频变换，利用频域变换系数构造训练字典，再将待测轴承振动信号的频域系数在该字典上进行稀疏分解，求取稀疏系数，根据重构误差的最小值确定故障类型。测试结果表明：该方法能有效克服噪声干扰，并避免故障特征频率的估算问题。