基于混合域特征集与加权KNN的滚动轴承故障诊断

Fault Diagnosis of Roller Bearing based on Hybrid Feature Set and Weighted KNN

针对滚动轴承的早期故障特征微弱难以有效辨识的问题,提出基于混合域特征集与加权K-近邻分类器的滚动轴承早期故障诊断方法。首先,基于时域、频域、时频域信号处理方法计算滚动轴承早期故障的特征指标量,构造混合域特征集,再将混合域特征集输入给KNN实现滚动轴承的早期故障诊断。实验结果表明,基于混合域特征集与加权K-近邻分类器的滚动轴承早期故障诊断方法能够有效地提取滚动轴承早期故障的低维敏感特征,而且结构稳定,诊断精度高,可以推广应用于滚动轴承的实时在线监测。

Aiming at the problem that the roller bearings early fault features are faint that difficult to be effectively identified,a fault diagnosis method of roller bearing based on hybrid feature set and weighted K- nearest- neighbor（ KNN） is proposed. Firstly,those early fault features of roller bearing are calculated based on the signal processing method in time domain,frequency domain and time- frequency domain to construct hybrid feature set. Then,those hybrid feature set are inputted into weighted K- nearest- neighbor for roller bearing early fault identification. The experimental results show that this proposed rolling bearing fault diagnosis method can effectively extract more sensitive early fault features,and the structure is stable,the diagnosis precision is high. It can be applied in the roller bearing real- time on- line monitoring.