The traditional method for identifying the fault characteristics of internal combustion engine crank-shaft bearings directly applies threshold denoising without collecting multi-sensor signals,resulting in poor rec-ognition 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 sig-nal 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 wave-let packet analysis.Design a comparative experiment,and the experimental results show that the fault feature recognition effect of this research method is better.
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