基于小波包分解的汽车发动机轴承故障诊断方法

Fault diagnosis method of automobile engine bearing based on wavelet packet decomposition

在汽车发动机轴承的故障诊断中,现有诊断方法产生的结果与实际情况间存在差异,其诊断准确性较差。对此,文章引入小波包分解对汽车发动机轴承的故障诊断方法展开了设计与研究。其中,利用小波包分解对故障信号进行了重构,通过重构信号提取了汽车发动机轴承的故障特征,结合故障特征频率对结果进行了识别,实现了轴承具体故障类型的诊断。通过对比实验发现,相较于现有诊断方法,文章提出的诊断方法产生的诊断结果与实际情况更接近,其诊断准确率更高,能够为轴承及汽车发动机的运行维护提供更加可靠的依据。

In the fault diagnosis of automotive engine bearings,there are differences between the results generated by existing diagnostic methods and the actual situation,resulting in poor diagnostic accuracy.In this regard,the article introduces wavelet packet decomposition to design and research fault diagnosis methods for automotive engine bearings.The fault signal was reconstructed using wavelet packet decomposition,and the fault features of automotive engine bearings were extracted through the reconstructed signal.The results were identified based on the frequency of the fault features,achieving the diagnosis of specific bearing fault types.Through comparative experiments,it was found that compared to existing diagnostic methods,the diagnostic results generated by the proposed diagnostic method in the article are closer to the actual situation,with higher diagnostic accuracy,and can provide a more reliable basis for the operation and maintenance of bearings and automotive engines.