Multi-Bolt looseness detection using a new acoustic emission strategy

Abstract

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).

CDMTNet: a novel transfer learning model for the loosening detection of mechanical structures with threaded fasteners

“…1 A sufficient preload is important to ensure the connection and fastening performance and improve product reliability. However, threaded fasteners are prone to loosening 2,3 due to exposure to harsh working environments, especially vibration environments. Loosening will directly decrease the preload, causing the threaded fasteners to lose connection ability, thus affecting the reliability, integrity, and durability of mechanical systems.…”

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

“…Ye et al 38 employed the random forest algorithm to predict vibration amplitudes of bridges during typhoon occurrence to avoid bridge damage and failure. Wang 39 developed a novel bolt looseness classification approach by integrating the acoustic energy method and dual shapelet networks. Table 1 summarizes the application of ML approaches for damage detection in real-life structures.…”

Percussion-based loosening detection method for multi-bolt structure using convolutional neural network DenseNet-CBAM

“…6 Regular loosening detection should be conducted to acquire the fastening states of threaded fasteners in advance and thus avoid structural failure and safety accident. [7][8][9] Loosening detection methods can be divided into three categories including sensor-based, [10][11][12][13][14][15][16][17][18][19][20][21][22][23] visionbased, [24][25][26][27][28][29] and percussion-based methods. [30][31][32][33][34][35][36][37][38][39][40] The first one installs sensors on mechanical structures and uses the changes of response signal to evaluate loosening states.…”

“…Loosening detection methods can be divided into three categories including sensor-based, 10–23 vision-based, 24–29 and percussion-based methods. 30–40 The first one installs sensors on mechanical structures and uses the changes of response signal to evaluate loosening states.…”

“…All the above studies are focused on single bolt structure. Wang 22 and Chen et al 11 respectively used acoustic emission technology and electromechanical impedance method to detect multi-bolt structures. However, the installment process of sensors is troublesome and time-consuming, and sensors may be not allowed to be installed in some cases.…”