Automatic bolt tightness detection using acoustic emission and deep learning

Bolts are typically deployed to unify disparate components in civil engineering structures, making the surveillance of their tightness critical to ensuring the stability of such structures. The innovation of this paper is the development of an automatic monitoring method for bolt tightness using acoustic emission (AE) and deep learning (DL). Initially, AE sensors are first used to monitor structural bolts, and AE signals are preprocessed by a continuous wavelet transform (CWT) to obtain different time-frequency components, which are then fed into the convolutional neural network (CNN). To reduce the amount of training parameters and enhance the capacity for feature extraction, transfer learning (TL) is introduced to train the model. In the experimental scenario, the bolts exhibit seven categories of tightness. The findings show that the proposed method can accurately classify the bolts' tightness, and can be used to monitor the early looseness of the bolts online.