Bolt Loosening Detection Based on Principal Component Analysis and Support Vector Machine

Shiwei Wu, Sisi Xing, Fei Du, Chao Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationNeural Computing for Advanced Applications - 3rd International Conference, NCAA 2022, Proceedings
EditorsHaijun Zhang, Yuehui Chen, Xianghua Chu, Zhao Zhang, Tianyong Hao, Zhou Wu, Yimin Yang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages286-300
Number of pages15
ISBN (Print)9789811961410
DOIs
StatePublished - 2022
Event3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022 - Jinan, China
Duration: 8 Jul 202210 Jul 2022

Publication series

NameCommunications in Computer and Information Science
Volume1637 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022
Country/TerritoryChina
CityJinan
Period8/07/2210/07/22

Keywords

  • Bolt loosening monitoring
  • Machine learning
  • SVM
  • Ultrasound-guided wave

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