Estimation of Pipe Wall Thinning Using a Convolutional Neural Network for Regression
- Title
- Estimation of Pipe Wall Thinning Using a Convolutional Neural Network for Regression
- Author
- 박준홍
- Keywords
- Pipe wall thinning; loop test; vibration characteristics; thickness prediction; convolutional neural network
- Issue Date
- 2022-06
- Publisher
- TAYLOR & FRANCIS INC
- Citation
- NUCLEAR TECHNOLOGY, v. 208, NO. 7, Page. 1184-1191
- Abstract
- A pipe wall thinning diagnosis method based on vibration characteristics is proposed. Elbow specimens with artificial pipe wall thinning were fabricated and combined in a loop. By running a pump in the loop, vibration was induced by flow, and the vibrational signals were measured with accelerometers. The effect of pipe wall thinning on the vibrational signals was investigated by analyzing the spectral data of the acceleration signals. The analyzed vibration characteristics were difficult to observe because the change in characteristics was small. A convolutional neural network (CNN) specialized for data recognition was applied to recognize the small change in vibrational signal resulting from the pipe wall thinning. A regression model based on CNN was chosen to learn the tendency of change in the vibrational signals with varying thinning. The data types advantageous for training the regression model were identified. An early stopping technique using the validation data set was adopted to regularize the regression model. The trained regression model was able to predict pipe thinning.
- URI
- https://www.tandfonline.com/doi/full/10.1080/00295450.2021.2018271https://repository.hanyang.ac.kr/handle/20.500.11754/177463
- ISSN
- 0029-5450;1943-7471
- DOI
- 10.1080/00295450.2021.2018271
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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