PCT-Net: A Multi-Scenario Noise-Adaptive Fusion Network for Bolt Loosening Detection

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring.