Multi-Bolt looseness detection using a new acoustic emission strategy

In mechanical and aerospace engineering, different components are usually integrated together via bolted connections. Compared to the rivet joint and welding joint, the bolted connection is preferred in some cases due to its easy-to-operation and low-cost. However, the bolt self-loosening caused by vibration or other issues (e.g., improper installation and chemical corrosion) may induce severe accidents. Therefore, in this paper, the author proposes a new strategy based on the acoustic emission (AE) technique to detect bolt looseness. To the best of the author’s knowledge, this research is the first attempt to identify multi-bolt looseness via the AE-based method. Particularly, the main contribution is that the author proposes a new shapelet-enhanced AE method that employs a newly developed dual-shapelet networks classifier to discriminate AE waves. The dual-shapelet networks classifier consists of sample-specific shapelets, which is sensitive to the difference among various categories, and category-specific shapelets derived from auxiliary binary classifiers. The objective of category-specific shapelets is to address the imbalanced classification task, that is, discriminating minority categories. Then, the sample-specific shapelets and category-specific shapelets are combined to extract features from AE signals under different multi-bolt looseness cases, and the final classification is achieved by feeding the extracted features into a softmax layer. Finally, the author conducts an experiment to verify the effectiveness of the proposed method. Moreover, by comparing the proposed method’s performance with two baselines, the advantages of the shapelet-enhanced AE method can be demonstrated. Overall, this research demonstrates that the AE technique is valid to characterize friction and collision between asperities on the bolted interface, thus providing a new direction for multi-bolt looseness detection, and the proposed shapelet-enhanced AE method has substantial potential in the field of structural health monitoring (SHM).