Embedded acoustic black hole for broadband weak signal enhancement in early bearing fault detection

Rolling element bearings are widely used in rotating machinery, and their early fault detection is of critical importance for condition monitoring and fault prevention. Existing early fault detection methods predominantly rely on complex signal processing algorithms such as spectral kurtosis, which search for the optimal resonance band and apply bandpass filtering to extract weak fault features. However, such methods inevitably discard valid information residing in other frequency bands during noise reduction, fundamentally limiting the detectability of weak fault features. To address this challenge, this paper proposes an embedded acoustic black hole (ABH)-assisted enhanced detection method, in which an ABH structure is integrated into a mechanical component to achieve broadband physical enhancement and efficient acquisition of weak fault features at the sensing front end. The wave focusing and amplitude amplification mechanism of the embedded ABH is elucidated through theoretical analysis, and its broadband enhancement capability for weak fault signals is validated by numerical simulation. Experimental verification is further performed on a rolling bearing test rig to assess the practical fault detection performance of the proposed method. The results show that, compared with conventional resonance demodulation methods based on spectral kurtosis, the proposed method enables near-full-band demodulation without requiring a complex frequency band selection process, significantly enhances the identifiability of weak bearing fault features, and fundamentally circumvents the bottleneck imposed by optimal frequency band selection. This paper offers a novel structural-level strategy for early bearing fault detection, with promising application prospects in rotating machinery condition monitoring and fault diagnosis.