Variational Bayesian Modal Composition Beamforming for fast-rotating axial-fan blade-noise localization and its application condition

The Modal Composition Beamforming (MCB) method can quickly achieve the localization of fast-rotating sound sources; however, its low resolution and unclear applied conditions severely limit its industrial application. This study aims to investigate the MCB-based high-resolution localization method for multiple rotating sound sources and apply it to the localization and identification of axial-fan blade-noise. In this paper, the Variational Bayesian Approximation (VBA) and the Subspace Variational Bayesian Approximation (SVB) methods are used to solve the MCB-based rotating sound power propagation (RSP) model, denoted as RSP-VBA and RSP-SVB, respectively. The effectiveness of the proposed RSP-VBA and RSP-SVB are experimentally validated for the first time. Compared to the conventional MCB method, the resolution is significantly improved, and the proposed high-resolution methods are even faster than the classical Rotating Source Identifier (ROSI) method in most conditions. More importantly, the applied condition of the MCB, RSP-VBA, and RSP-SVB methods is given and verified by using three evaluation indicators. Then a schematic of the applied condition with examples is provided. With the guide of the applied conditions, the RSP-VBA and RSP-SVB methods are applied to the blade-noise localization of various multiblade high-speed axial fans.