A time-frequency spectral amplitude modulation method and its applications in rolling bearing fault diagnosis

As one of the key components in rotating machinery, rolling bearings can affect the running state of equipment and even cause huge damage. Therefore, various methods have been proposed to improve the processing of bearing fault signals. Among them, spectral amplitude modulation is an empirical and automated method that can extract the fault characteristics of rolling bearings effectively. However, the characteristic frequencies can be no longer evident or even overwhelmed due to strong background noise. In this paper, a time-frequency spectral amplitude modulation (TFSAM) method is proposed. The proposed method obtains the amplitude in the time-frequency domain using short-time Fourier transform and modifies them with different weights. Thus, more accurate and detailed information about the amplitude can be calculated. Meanwhile, an indicator is proposed to select the optimal weight automatically, after which more obvious characteristic frequencies can be spotted. The effectiveness of the method is verified by a simulated signal and applied to rolling bearing fault diagnosis of outer ring, inner ring and compound faults respectively. The results indicate its accuracy and effectivity in identifying fault information. Comparisons with other commonly used techniques verify the advantages of the proposed method.