A Fabry-Pérot resonator based metamaterial structure for acoustic signal enhancement in machinery condition monitoring

Abstract

Lowering operational frequencies in acoustic signal processing enhances maintenance efficiency and reduces data handling burden for online machine condition monitoring. However, the lower frequency range presents challenges in acoustic sensing due to longer wavelengths requiring larger acoustic-aided devices and the difficulty in detecting subtle fault signals within surrounding noise. This study introduces a novel sensing approach that combines an acoustic wave-compressing graded index metamaterial with a Fabry-Pérot resonator to achieve machine fault acoustic signal enhancement. This innovative method amplifies lower frequency acoustic signals while maintaining the same compact dimensions as current graded metamaterials. Analytical models establish a direct link between sound pressure gain and key parameters, guiding tailored amplification for machinery fault detection. Numerical simulations and prototype experiments reveal a significant reduction in operational frequency and increased amplification gain, demonstrating the design’s effectiveness in improving lower frequency detection while remaining compact. The methodology’s efficacy is further demonstrated by its ability to extract weak fault harmonics in gear and bearing diagnostics. This approach contributes to acoustic signal selective-frequency range amplification and operational frequency lowering in acoustic-aided devices, opening avenues for application in low-speed rotational machinery condition monitoring, with potential impact extending to fields such as device miniaturization for enhanced fault detection in compact systems.