A force-adaptive percussion method for bolt looseness assessment

Percussion-based methods have attracted growing interest in the assessment of bolt looseness. Nevertheless, their suitability for field applications is constrained by the irregularity in manual percussion force. Variabilities in percussion forces can distort the characterization of signals, resulting in an insufficient assessment of bolt looseness. In response to this challenge, the paper introduces a force-adaptive percussion method that utilizes sound phase as a feature, theoretically demonstrating its resilience to percussion force irregularities for the first time. Verification experiments were conducted on a standard beam-column bolted joint. Experimental results showed that phase features of varied percussion signals under identical preload conditions exhibit good consistency, in contrast to the Mel-frequency cepstral coefficients (MFCCs), another prevalent characteristic feature. To assess the effectiveness of the proposed strategy, a residual structure-integrated network was applied for bolt looseness assessment using both phase features and the MFCCs. The results indicated that the model trained with phase features attained higher classification accuracy and superior generalization capability compared to another model trained with MFCCs. These findings substantiated the validity and superiority of the proposed method, indicating its potential to substantially enhance the applicability of field bolt looseness assessment.