Research on acoustic fault diagnosis of bearings based on spatial filtering and time-frequency domain filtering

Abstract

In conventional methods, vibration-based diagnosis (VBD) is commonly used for identifying rolling bearing faults. In contrast, acoustical-based fault diagnosis (ABD) has been extensively studied as an alternative due to its non-contact measurement capabilities, which are superior to VBD. Nonetheless, the accuracy of machine diagnosis may be compromised by multisource aliasing in the recorded acoustic signals. This research integrates spatial domain filtering and time-frequency domain filtering to address the issue of bearing fault diagnosis. By utilizing the second-order cone optimization method, differential power response beamformers have been established to attain a high signal-to-noise ratio of bearing sound while effectively controlling the noise power level. The output of differential beamformers is analyzed using the parametric adaptive MOMEDA approach. The method presented in this paper enables bearing acoustic fault diagnosis and identification of the faulty bearing location. The validity of the proposed ABD method has been demonstrated through experimental and simulation results.