Rotating machinery fault acoustic source localization using reduced-rank cyclic regression and microphone array

The key to implementing acoustic diagnostic technology is to separate the target acoustic source from multiple sources in complex scenarios. This paper proposes a method for localizing fault acoustic sources in rotating machinery, which is based on reduced-rank cyclic regression and acoustic arrays. First, the cyclic spectral density technique is utilized to determine the cyclic frequency of the acoustic source in the rotating machine. Subsequently, the signal of interest corresponding to this cyclic frequency is separated using the reduced-rank cyclic regression method. By integrating this approach with conventional beamforming technology, it is possible to localize fault acoustic sources in rotating machinery. Numerical simulations and experiments are conducted to validate the proposed method. To investigate potential applications, the localization of rolling bearings with inner ring faults was assessed, and the findings indicated that the R-CBF method efficiently mitigates noise interference in complex environments, surmounting the constraint of conventional beamforming in distinguishing cyclostationary acoustic sources.