Recently, the issue of bolt looseness has attracted more attention due to its severe consequences. Among different methods for bolt looseness detection, the active sensing method that is based on stress wave signals is preferred since it is low cost and high robust. However, current active sensing method depends on permanent contact sensors, which may be impractical. Moreover, the investigation of multi-bolt looseness detection via the active sensing is very limited so far. With the above deficiency in mind, we propose a new robotic-assisted active sensing method based on our newly designed PZT-enabled smart gloves (SGs) and position-based visual servoing (PBVS) technique. Particularly, another main contribution is that we develop a new Siamese CapsNet to classify stress wave signals under different cases for multi-bolt looseness detection. Compared to machine learning (ML) and traditional deep learning techniques such as Convolutional Neural Networks (CNN), the proposed Siamese CapsNet model can achieve better performance and realize the recognition of signals that is never used during the training, which is impossible for common classification methods. Finally, an experiment is conducted to verify the effectiveness of the proposed method and Siamese CapsNet, which can guide future research significantly.
