Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Vibration and Control

DOI: 10.1177/10775463251332984

Authors: Junjian Hou, Song Chen, Liang Yu, Yudong Zhong, Wenbin He

Institutions: Zhengzhou University of Light Industry, Northwestern Polytechnical University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,400

Funder: National Natural Science Foundation of China