TY - GEN
T1 - Bolt Loosening Detection Based on Principal Component Analysis and Support Vector Machine
AU - Wu, Shiwei
AU - Xing, Sisi
AU - Du, Fei
AU - Xu, Chao
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Online monitoring of bolt preload is essential to ensure the proper functioning of bolted structures. Ultrasonic guided wave has the advantages of high sensitivity and wide monitoring range, so it is widely used in the study of bolt loosening monitoring. However, the propagation mechanism of ultrasonic guided waves in bolted connection structure is complicated, and it is difficult to establish a direct relationship between guided wave signal and bolt loosening state directly. In recent years, machine learning and other artificial intelligence technologies have flourished, and a more effective bolt loosening detection technique can be established by using machine learning combined with the principle of guided wave damage detection. In this paper, a bolt loosening identification method based on principal component analysis (PCA) and support vector machine (SVM) is proposed, aiming to achieve end-to-end bolt loosening monitoring with few samples. The ultrasonic wave-guided experimental results of the bolted joint lap plate show that the proposed PCA and SVM technique achieves a loosening recognition accuracy of 92.5%, which is higher than other machine learning methods, and the effects of signal length, number of principal components and the choice of kernel function on the classification performance are explored.
AB - Online monitoring of bolt preload is essential to ensure the proper functioning of bolted structures. Ultrasonic guided wave has the advantages of high sensitivity and wide monitoring range, so it is widely used in the study of bolt loosening monitoring. However, the propagation mechanism of ultrasonic guided waves in bolted connection structure is complicated, and it is difficult to establish a direct relationship between guided wave signal and bolt loosening state directly. In recent years, machine learning and other artificial intelligence technologies have flourished, and a more effective bolt loosening detection technique can be established by using machine learning combined with the principle of guided wave damage detection. In this paper, a bolt loosening identification method based on principal component analysis (PCA) and support vector machine (SVM) is proposed, aiming to achieve end-to-end bolt loosening monitoring with few samples. The ultrasonic wave-guided experimental results of the bolted joint lap plate show that the proposed PCA and SVM technique achieves a loosening recognition accuracy of 92.5%, which is higher than other machine learning methods, and the effects of signal length, number of principal components and the choice of kernel function on the classification performance are explored.
KW - Bolt loosening monitoring
KW - Machine learning
KW - SVM
KW - Ultrasound-guided wave
UR - http://www.scopus.com/inward/record.url?scp=85142741155&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-6142-7_22
DO - 10.1007/978-981-19-6142-7_22
M3 - 会议稿件
AN - SCOPUS:85142741155
SN - 9789811961410
T3 - Communications in Computer and Information Science
SP - 286
EP - 300
BT - Neural Computing for Advanced Applications - 3rd International Conference, NCAA 2022, Proceedings
A2 - Zhang, Haijun
A2 - Chen, Yuehui
A2 - Chu, Xianghua
A2 - Zhang, Zhao
A2 - Hao, Tianyong
A2 - Wu, Zhou
A2 - Yang, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022
Y2 - 8 July 2022 through 10 July 2022
ER -