A probabilistic approach with hierarchical prior for duct acoustic mode identification of broadband noise

Fan noise is a predominant noise component of the aero-engine, which consists of tonal noise and broadband noise. Duct mode calculation of random broadband noise is more complex than that of tonal noise. A probabilistic approach for duct acoustic mode identification is proposed to identify duct modes of broadband fan noise with high efficiency and accuracy. The inverse problem of mode identification is represented by a Bayesian framework based on a Gaussian-scale mixture prior model. Computational complexity and consuming time are remarkably decreased by approximating the Gaussian likelihood through a surrogate function. The block coordinate descent algorithm in the majorization–minimization framework is adopted for the indirect and iterative calculation of the mode coefficients. Simulations and experiments have verified the high computational efficiency afforded by the method, which provides more accurate results for duct mode identification of broadband noise, removes the influence of interfering modes on the recognition results, and allows better observation of the characteristics of the mode in a wide range of frequencies and rotating speeds.