<i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence
This paper describes a non-destructive detection method for identifying cable defects using <i>K</i>-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Inventions |
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| Online Access: | https://www.mdpi.com/2411-5134/8/6/132 |
| _version_ | 1797380627111084032 |
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| author | Brijesh Patel Zih Fong Huang Chih-Ho Yeh Yen-Ru Shih Po Ting Lin |
| author_facet | Brijesh Patel Zih Fong Huang Chih-Ho Yeh Yen-Ru Shih Po Ting Lin |
| author_sort | Brijesh Patel |
| collection | DOAJ |
| description | This paper describes a non-destructive detection method for identifying cable defects using <i>K</i>-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, <i>K</i>-mer frequency encoding, and artificial intelligence-based identification. A magnetic leakage detection device detects signals via sensors and records their corresponding positions to obtain cable signals. The <i>K</i>-mer frequency encoding method consists of several steps, including cable signal normalization, the establishment of <i>K</i>-mer frequency encoding, repeated sampling of cable signals, and conversion for comparison to derive the <i>K</i>-mer frequency. The <i>K</i>-mer frequency coding method has advantages in data processing and repeated sampling. In the identification step of the artificial intelligence identification model, an autoencoder model is used as the algorithm, and the <i>K</i>-mer frequency coding method is used to introduce artificial parameters. Proper adjustments of these parameters are required for optimal cable defect identification performance in various applications and usage scenarios. Experiment results show that the proposed <i>K</i>-mer frequency encoding method is effective, with a cable identification accuracy rate of 91% achieved through repeated sampling. |
| first_indexed | 2024-03-08T20:39:58Z |
| format | Article |
| id | doaj.art-8e4db8a8d0ff43b480d3f0b9061db915 |
| institution | Directory Open Access Journal |
| issn | 2411-5134 |
| language | English |
| last_indexed | 2024-03-08T20:39:58Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Inventions |
| spelling | doaj.art-8e4db8a8d0ff43b480d3f0b9061db9152023-12-22T14:16:27ZengMDPI AGInventions2411-51342023-10-018613210.3390/inventions8060132<i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial IntelligenceBrijesh Patel0Zih Fong Huang1Chih-Ho Yeh2Yen-Ru Shih3Po Ting Lin4Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanThis paper describes a non-destructive detection method for identifying cable defects using <i>K</i>-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, <i>K</i>-mer frequency encoding, and artificial intelligence-based identification. A magnetic leakage detection device detects signals via sensors and records their corresponding positions to obtain cable signals. The <i>K</i>-mer frequency encoding method consists of several steps, including cable signal normalization, the establishment of <i>K</i>-mer frequency encoding, repeated sampling of cable signals, and conversion for comparison to derive the <i>K</i>-mer frequency. The <i>K</i>-mer frequency coding method has advantages in data processing and repeated sampling. In the identification step of the artificial intelligence identification model, an autoencoder model is used as the algorithm, and the <i>K</i>-mer frequency coding method is used to introduce artificial parameters. Proper adjustments of these parameters are required for optimal cable defect identification performance in various applications and usage scenarios. Experiment results show that the proposed <i>K</i>-mer frequency encoding method is effective, with a cable identification accuracy rate of 91% achieved through repeated sampling.https://www.mdpi.com/2411-5134/8/6/132<i>K</i>-mer frequency encodingcable defectsnon-destructive testingartificial intelligence |
| spellingShingle | Brijesh Patel Zih Fong Huang Chih-Ho Yeh Yen-Ru Shih Po Ting Lin <i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence Inventions <i>K</i>-mer frequency encoding cable defects non-destructive testing artificial intelligence |
| title | <i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence |
| title_full | <i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence |
| title_fullStr | <i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence |
| title_full_unstemmed | <i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence |
| title_short | <i>K</i>-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence |
| title_sort | i k i mer frequency encoding model for cable defect identification a combination of non destructive testing approach with artificial intelligence |
| topic | <i>K</i>-mer frequency encoding cable defects non-destructive testing artificial intelligence |
| url | https://www.mdpi.com/2411-5134/8/6/132 |
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