基于小波包分析的内燃机曲轴轴承故障特征识别

Identification of Crankshaft Bearing Fault of Internal Combustion Engine Based on Wavelet Packet Analysis

传统内燃机曲轴轴承故障特征识别方法直接进行阈值降噪未进行多传感器信号采集,造成传统方法识别效果较差,提出基于小波包分析的内燃机曲轴轴承故障特征识别。对内燃机曲轴轴承故障多传感器信号进行采集,提高信号处理的效率和准确性,基于小波包分析进行阈值降噪,设计故障特征识别流程,实现基于小波包分析的内燃机曲轴轴承故障特征识别。设计对比实验,实验结果表明,该研究方法故障特征识别效果更好。

The traditional method for identifying the fault characteristics of internal combustion engine crankshaft bearings directly applies threshold denoising without collecting multi-sensor signals,resulting in poor recognition performance of traditional methods.Therefore,a wavelet packet analysis based method for identifying the fault characteristics of internal combustion engine crankshaft bearings is proposed.Collecting multi-sensor signals for crankshaft bearing faults in internal combustion engines to improve the efficiency and accuracy of signal processing,threshold denoising based on wavelet packet analysis,designing a fault feature recognition process,and achieving fault feature recognition of internal combustion engine crankshaft bearings based on wavelet packet analysis.Design a comparative experiment,and the experimental results show that the fault feature recognition effect of this research method is better.