摘要
针对发动机加速振动信号的非平稳性和特征参数的模糊性特点,提出整工作循环阶比跟踪谱和模糊C均值算法相结合的方法。首先将加速信号进行阶比重采样,然后把发动机一个工作循环的信号作为一个间隔计算转速,再将这些转速下的阶比谱图放在一起构成阶比跟踪谱,通过计算不同阶比带的累加能量作为故障特征向量,对这些特征向量进行归一化和模糊聚类,得到分类矩阵和聚类中心,最后通过计算待测故障样本与已知故障样本聚类中心的贴近度实现故障模式识别。故障诊断实例表明,该方法能有效地诊断发动机曲轴轴承的故障。
In view of the non-stationary feature of engine acceleration vibration signals and the fuzziness of characteristic parameters,a method with combination of work-cycle order tracking spectrum and fuzzy C-mean clustering is proposed. Firstly the order re-sampling is conducted on acceleration signals,the rotation speed is calculated based on a work cycle of engine,and the order spectra at different speeds are put together to compose an order tracking spectrum. The accumulated energy of different order bands are calculated as eigenvectors,which are then normalized and fuzzily clustered to obtain classification matrix and clustering centers. Finally the fault pattern is identified by calculating Hamming approach degrees between the clustering centers of known fault samples and fault samples to be detected. The real examples of fault diagnosis show that the method proposed can effectively diagnose the faults of engine crankshaft bearings.
出处
《汽车工程》
EI
CSCD
北大核心
2014年第8期1024-1028,共5页
Automotive Engineering
基金
总装备部预研项目(40407030302)资助
