Acoustic signal analysis for gear fault diagnosis using a uniform circular microphone array

In this paper, a far-field acoustic signal processing method based on a uniform circular microphone array is proposed for the gear fault detection. The method takes ensemble empirical mode decomposition (EEMD) as a preprocessing approach, and the estimation of signal parameters via rotational invariance techniques (ESPRIT) is applied as the beamformer, which offers an adaptive and convenient approach to solve the serious aliasing and distortion in acoustic signals. The method greatly reduces the inherent demands for the microphone numbers and the computational load while holding a satisfying accuracy, making it more promising in practical engineering applications. Besides, aiming at the situation that gear failures cannot be judged solely by gear meshing frequencies (GMF) and sound source locations, seventeen statistical feature parameters are applied to the processed signals for the fault severity recognition, and six of them are found efficient, which provides a further reference for acoustic gear diagnosis.