An aeroengine fault diagnosis method using high-frequency acoustic coupling with rotating blade flow field

Abstract

Utilizing high-frequency sound waves for aeroengine structure diagnosis has been established in previous works. However, exploring its potential in analyzing rotating blade flow fields remains an open area of research. In this study, we propose a non-invasive fault diagnosis method that couples high-frequency sound waves with rotating blade flow fields, performing mode detection of the coupled frequency. As a widely used noise detection technique, acoustic mode detection enables the extraction of spatial information from noise, providing essential insights for noise reduction design. Essentially, the flow-sound coupling arises from nonlinear interactions, wherein the characteristic low-frequency periodic flow is excited by the high-frequencyexternal sound source, generating new coupled acoustic sources that radiate and propagate inside the duct. Mode detection aids in understanding the location and spatial phase information of the coupled acoustic sources. Furthermore, through both simulations and experiments, we have established the connection between fault information and mode spectrum. Finally, we endeavor to employ a machine learning approach to build a fault diagnosis model. In summary, this method ingeniously superimposes low-frequency periodic flow field information onto high-frequency sound waves, achieving high-precision diagnosis of the rotating blade flow field.