Prior knowledge-guided multi-scale acoustic metamaterial sensing for gearbox weak fault signal detection

Abstract

The early fault detection presents a significant challenge due to the intricate structure of the gearbox, substantial noise interference, and multi-component coupling modulation. Traditional post-processing algorithms are relatively complex and inefficient. Motivated by the properties of acoustic metamaterial in feature enhancement and amplitude-frequency modulation mechanism of signal processing, this study proposes multi-scale acoustic metamaterials (MSAM) for gearbox weak fault signal detection with multi-scale feature information synthesized. Specially, benefiting from the merits of acoustic rainbow capture in amplitude gain and noise suppression, this front-end enhanced sensing approach exploits the properties of acoustic compression and feature separation of different frequency components of sound waves. Guided by prior knowledge of gearbox modulation mechanisms, the acoustic metamaterial structure is firstly optimized and miniaturized, followed by experimental testing of the center frequency and bandwidth of each air gap. Notably, the single air gap of this designed MSAM is verified that an amplitude gain exceeding 10 times for target components at a single scale can be achieved according to the results of fault simulation signal testing. Thereupon, focusing on issue of multi-scale coupling modulation, two cases has been also provided to illustrate the ability of multi-scale feature extraction with three adjacent air gaps and two non-adjacent gaps from MSAM. These indicate that the proposed front-end enhanced sensing structure can provide a more comprehensive and distinct representation than that of fault characteristics obtained from free-field collected signals even under strong noise and complex multi-scale coupling interferences. It can be foreseen that the proposed mechanical signal sensing driven with acoustic metamaterial brings great potential in weak signal detection, and it also shows the expectation of achieving variable scale adaptive control and material intelligent sensing.