ResNet-integrated very early bolt looseness monitoring based on intrinsic feature extraction of percussion sounds

Abstract Very early bolt looseness monitoring has been a challenge in the field of structural health monitoring. The authors have conducted a further study of the previous researches, with the aim of detecting very early bolt looseness conditions. The intrinsic features of vibro-acoustic signals contain the underlying dynamic characteristics denoting full range bolt looseness conditions. Correspondingly, this paper proposes a novel ResNet-integrated very early bolt looseness monitoring approach based on intrinsic feature extraction of percussion sounds. The intrinsic features of percussion-caused sound signals were extracted by variational mode decomposition (VMD), where the parameters of VMD were determined by grey wolf optimization algorithm. The optimal band-limited intrinsic mode functions were converted into two-dimensional time–frequency maps by continuous wavelet transform. The (red green blue) RGB images were adopted as the input of residual network (ResNet) to monitor very early bolt looseness conditions. The results and analysis illustrate the validity and superiority of the novel ResNet-integrated very early bolt looseness monitoring approach. The proposed approach in our researches provides a novel solution for very early bolt looseness monitoring in the field of structural health monitoring. The strategy can also be extended to other nonlinear signal processing-involved fields.