An adaptive time–frequency demodulation method and its applications in rolling bearing fault diagnosis

Abstract

Abstract Rolling bearings are critical and easily damaged components of mechanical equipment. In practical engineering applications, the collected signals typically contain a large amount of noise, which makes fault diagnosis difficult. Based on this, this paper proposes an adaptive time–frequency demodulation method for rolling bearing diagnosis. The proposed method first obtains the complex envelope of the vibration signal in the time–frequency domain using the S transform (ST), and the time–frequency coefficient of ST can be used as the complex envelope, which is proved in detail in this paper. Subsequently, the complex envelope of the optimal slice frequency is obtained by frequency slicing to significantly weaken the interference of irrelevant noise and highlight the fault characteristics. An indicator is proposed to adaptively select an optimal slice frequency component that contains the most fault information. Finally, the slice envelope spectrum of the optimal slice frequency is obtained using Fourier transform for fault diagnosis. The feasibility of the proposed method is verified using the simulated signal. The application results of the bearing inner and outer ring fault experimental signals indicate that the proposed method is more accurate and effective for bearing fault diagnosis. Comparisons with other commonly used methods also verified the superiority of the proposed method.