Research on a percussion-based bolt looseness identification method based on phase feature and convolutional neural network
Pengtao Liu, Xiaopeng Wang, Tianning Chen, Yongquan Wang, Feiran Mao, Wenhang Liu
Abstract
Abstract The percussion-based method has become a hot spot for bolt looseness monitoring due to its advantages of non-contact sensing, portability, and low cost. However, the features of bolt looseness in percussion methods lack phase information. In this paper, a percussion method based on the all-pole group delay function in the phase domain is proposed for the first time, and the bolt looseness is determined by a convolutional neural network. Under the four signal-to-noise ratio levels (0, 2, 4 and 6 dB), the accuracy of the proposed method is 90.25%, 92.75%, 93.5% and 94%. The experiment proves the percussion audio signal of the structural point away from the bolt can reflect the looseness of the bolt. The phase feature can represent the information of bolt looseness and has fast training speed and high recognition accuracy, which is suitable for detecting bolt looseness torque.