| dc.description.abstract | Currently, the detection of bolt connections in practical applications often involves inspectors using rubber hammers to percuss the bolts and check if they are loosened based on the generated sound. However, this manual detection process is time-consuming, requires subjective judgment by the inspectors, and may be difficult or even dangerous to access certain detection sites.
To reduce the manpower and time costs of inspections, enhance the safety of inspectors, and improve the accuracy of detection through an objective approach, this study applies the concept of Tiny Machine Learning (TinyML) by deploying Mel-frequency cepstral coefficients (MFCCs) and Convolutional Neural Networks (CNNs) to a microcontroller. A bolt-loosen detection system based on a robotic arm and real-time audio recognition capabilities is developed for detecting loosen bolts. The bolts are categorized into two types: loosen and tight. The audio signals generated by knocking the bolts are subjected to feature extraction using MFCCs to obtain feature maps, which are then input to the CNN for training. After training, the model is combined with MFCCs to form an acoustic recognition model. The acoustic recognition model is then deployed to a microcontroller equipped with a microphone module, creating an AI acoustic recognition module. The AI acoustic recognition module is combined with a robotic arm and a tiny knocking device and a main controller for experimental verification.
The trained acoustic recognition model achieved an accuracy of 100% on the validation set and 99.57% on the test set. The AI acoustic recognition module deployed to the microcontroller achieved an accuracy of 75.7% and a recall rate of 77.3% in low environmental noise, and an accuracy of 72% and a recall rate of 68.7% in high environmental noise.
In the future, using this system, personnel will not need to perform the percussing manually; instead, they can remotely operate the mechanical arm for tapping and recognition, reducing the risks associated with inspections and providing an objective and fast way to determine whether the bolts are loosened. | en_US |