关键词: 经验小波变换, 最大峭度-包络谱熵, 齿轮箱, 故障诊断

Abstract: For the early faults of gearboxes, the fault features were not obvious, and the vibration signals were nonlinear and non-stationary, a method was proposed based on energy aggregation and EWT (EA-EWT). Vibration signals were decomposed by EA-EWT, and the maximum kurtosis envelope spectrum entropy criterion was used to filter the sensitive signals. For the selected signals, the minimum entropy deconvolution was used to reduce the noise. The Hilbert envelope spectrum of the signals was analyzed after noise reduction, and the fault types were identified by the frequency components in the envelope spectrum to realize the early fault diagnosis. The experimental results show that the new method may significantly enhance the early weak fault characteristics and improve the early fault diagnosis performances.

Key words: empirical wavelet transform(EWT), maximum kurtosis and envelope spectrum entropy, gearbox, fault diagnosis