Prediction of bolt fastening state using structural vibration signals
- Authors
- Jeong, Seong-Pil; Sohn, Jung Woo
- Issue Date
- Aug-2019
- Publisher
- KOREAN SOC MECHANICAL ENGINEERS
- Keywords
- Bolt fastening state; Classification; Machine learning; Piezoelectric sensor; Prognostics; Structural vibration
- Citation
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.33, no.8, pp 3963 - 3970
- Pages
- 8
- Journal Title
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
- Volume
- 33
- Number
- 8
- Start Page
- 3963
- End Page
- 3970
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/25522
- DOI
- 10.1007/s12206-019-0741-z
- ISSN
- 1738-494X
1976-3824
- Abstract
- We have proposed a new method to predict the state of bolt fastening connection using time-domain structural vibration signal and experimentally validated its effectivness. To obtain the structural vibration signal, non-contact type laser displcement sensor and contact type piezo film sensor were used, respectively. Two-beam structures with holes were prepared and fastened with a set of bolt and nut. By applying a random initial displacement at the free end of the cantilever beam structure, vibration signals were measured for three different bolt fastening states: fully fastened, half-loosened and 90 %-loosened. After extraction of features from the obtained vibration signals, the bolt fastening state was classified based on the k-nearest neighbor (k-NN) algorithm. It is experimentally verified that the bolt fastening state can be accurately predicted by using the structural vibration signals and machine learning algorithm.
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- Appears in
Collections - Department of Mechanical Design Engineering > 1. Journal Articles
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