Sensing with Sound Enhanced Acoustic Metamaterials for Fault Diagnosis

Cost-effective technology for condition monitoring and fault diagnosis is of practical importance for equipment maintenance and accident prevention. Among many fault diagnosis methods, sound-based sensing technology has been highly regarded due to its rich information, non-contact and flexible installation advantages. However, noise from the environment and other machines can interfere with sound signals, decreasing the effectiveness of acoustic sensors. In this paper, a novel trumpet-shaped acoustic metamaterial (TSAM) with a high enhancement of sound wave selection is proposed to detect rotating machinery faults. Firstly, a numerical calculation was carried out to test the characteristics of the proposed metamaterials model. Secondly, a finite element simulation was implemented on the model to verify the properties of the designed metamaterials. Finally, an experiment was conducted based on an electrical fan to prove the effectiveness of the designed metamaterials. The results of the signal-to-noise ratio show more than 25% improvement, consistently demonstrating the potentiality of the designed acoustic metamaterials for enhancing the weak fault signal in acoustic sensing and the capabilities of contributing to a more cost-effective fault diagnosis technology