A novel percussion-based method for multi-bolt looseness detection using one-dimensional memory augmented convolutional long short-term memory networks. (December 2021) - Wang, Furui; Song, Gangbing
Highlights: A new deep learning based percussion method is developed to detect bolt looseness. A new 1D-MACLSTM networks is developed to process percussion-induced sound signal. Compared to current methods, 1D-MACLSTM has better performance. A set of experiments were conducted to verify the proposed method in this paper. Abstract: In the past decade, bolt looseness detection has attracted much attention. Compared to common approaches that require the implementation of constant-contact sensors, several percussion-based methods have demonstrated their superiorities, including low-cost and easy-to-operate, in detecting bolt looseness. However, some drawbacks may impede the further real-world application of percussion-based methods in detecting bolt looseness. First, current percussion-based methods depend on hand-crafted features, which require the extensive experience of operators. In addition, the ability of current percussion-based methods in anti-noising and adaptability is unknown, since no related investigation has been conducted. Moreover, only single-bolt looseness is considered in the current percussion-based investigation. With these deficiencies in mind, in this paper, we propose a novel percussion-based method that uses a newly developed one-dimensional memory augmented convolutional long short-term memory (1D-MACLSTM) networks. Via the convolutional operation in the 1D-MACLSTM, we can avoid manual feature extraction, and the long short-term memory (LSTM) controller backed by external memory can enhance the ability of anti-noising and adaptability. Finally, three case studies are conducted on a pair of typical multi-bolt connections to verify the effectiveness of the proposed method, which has better performance than current percussion-based methods, particularly in a noisy environment and new scenarios.