Abstract: In order to solve the problem that existing methods are affected by fundamental frequency and harmonic frequency oscillation of new energy vehicle engine, a new energy vehicle engine fault detection method based on wavelet transform and support vector machine is proposed. Firstly, a detection model of abnormal noise signal of automobile engine fault is established, and the time-frequency parameters of basis function are adjusted adaptively. Then, the mechanical excitation component and the battery excitation component in the engine surface radiation noise are separated, and the new energy vehicle engine fault signal is decomposed by feature decomposition and multi-scale separation. Finally, wavelet transform combined with support vector machine algorithm was used to extract fault features of new energy vehicles, fuzzy clustering was carried out, and time-frequency analysis of fault signals was carried out in fractional Fourier domain to realize fault detection of new energy vehicle engines. The test results show that the method has high fault identification level and good clustering of fault feature points for new energy vehicle engine fault detection, and can effectively improve the ability of engine fault detection with good application effect.
Keywords: Wavelet transform, support vector machine, new energy vehicle, engine failure, fault detection
