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Fault Diagnosis of Backfill and Contact Status Using CNN and CWT
- Author(s)
- 이지윤
- Advisor
- 최수영
- Department
- 일반대학원 수학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-08
- Language
- eng
- Keyword
- Backfill; Continuous Wavelet Transform; Convolutional Neural Networks; Non-destructive Testing
- Alternative Abstract
- The structural collapse has led to many casualties in recent years. For this reason, significant research interest has been directed to evaluating the safety conditions of structures, an extension of the structural lifecycle, and the prevention of human casualties. This study examines socially required skills such as saving, safety, efficiency, and accuracy of structures. Typically, non-destructive testing (NDT) is used to inspect defects without modifying the state of the structure. Moreover, we examine the technology that incorporates the impact-echo method, which is by incorporating the NDT technique into machine learning. Among many structures, the defects are analyzed for the inner wall of the tunnel. With the help of Prof. Suyoung Choi of Ajou University and HBC Inc., we collected signal data and converted it into electrical signals, using an oscilloscope to measure physical vibrations caused by a mechanical impact of the structure. To prevent the regularity of the data according to the hitting position, the hitting position was randomly performed, and the data ratios of the hitting positions A, B, and C were 2:3:5. First of all, the study showed a performance of approximately 70% when Convolutional Neural Network (CNN) was analyzed with this one-dimensional time series data. We use two-dimensional image data obtained through continuous wavelet transformation (CWT) to improve the model performance. The image data showed a difference, according to the presence or absence of backfill, unobserved in the one-dimensional data. We distinguished between area C without a backfill and area A with a backfill, and the difference between areas A and B with a backfill was minimal. However, the CNN model showed some predictability. The final performance of our built model achieved 95.38%. The F1-score, which determined the data imbalance problem with the hitting position, achieved 94.21%.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Mathematics > 3. Theses(Master)
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