Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve
Abstract The accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-03-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.13147 |
| _version_ | 1797237699840573440 |
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| author | Qingguo Pan Yan Zhao Zheng Zhao Peng Lin |
| author_facet | Qingguo Pan Yan Zhao Zheng Zhao Peng Lin |
| author_sort | Qingguo Pan |
| collection | DOAJ |
| description | Abstract The accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy of fault detection using the VI curve. First, a loss function with multiple objectives is formulated by integrating curve fitting and prediction interval. The aim is to achieve the curve fitting between current and voltage while simultaneously determining the optimal upper and lower bounds of the prediction interval. Second, an ensemble approach is employed to reduce model uncertainty and derive the ultimate current predictions and intervals. These predictions and intervals are then used in a comparative approach to automatically detect faults in circuit boards. Finally, the effectiveness of the proposed algorithm in improving the accuracy of fault detection is verified on experimental circuit boards. |
| first_indexed | 2024-04-24T17:23:54Z |
| format | Article |
| id | doaj.art-929847cec7774d0fa431cd332b8133bd |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-04-24T17:23:54Z |
| publishDate | 2024-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-929847cec7774d0fa431cd332b8133bd2024-03-28T07:44:34ZengWileyElectronics Letters0013-51941350-911X2024-03-01606n/an/a10.1049/ell2.13147Constructing prediction intervals for circuit board fault detection: A neural network approach using VI CurveQingguo Pan0Yan Zhao1Zheng Zhao2Peng Lin3Wuhu State‐Owned Factory of Machining Wuhu Anhui ChinaArtificial Intelligence Institute Hangzhou Dianzi University Hangzhou ChinaArtificial Intelligence Institute Hangzhou Dianzi University Hangzhou ChinaArtificial Intelligence Institute Hangzhou Dianzi University Hangzhou ChinaAbstract The accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy of fault detection using the VI curve. First, a loss function with multiple objectives is formulated by integrating curve fitting and prediction interval. The aim is to achieve the curve fitting between current and voltage while simultaneously determining the optimal upper and lower bounds of the prediction interval. Second, an ensemble approach is employed to reduce model uncertainty and derive the ultimate current predictions and intervals. These predictions and intervals are then used in a comparative approach to automatically detect faults in circuit boards. Finally, the effectiveness of the proposed algorithm in improving the accuracy of fault detection is verified on experimental circuit boards.https://doi.org/10.1049/ell2.13147circuit boardsfault diagnosisneural networkprediction intervalsVI curve |
| spellingShingle | Qingguo Pan Yan Zhao Zheng Zhao Peng Lin Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve Electronics Letters circuit boards fault diagnosis neural network prediction intervals VI curve |
| title | Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve |
| title_full | Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve |
| title_fullStr | Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve |
| title_full_unstemmed | Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve |
| title_short | Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve |
| title_sort | constructing prediction intervals for circuit board fault detection a neural network approach using vi curve |
| topic | circuit boards fault diagnosis neural network prediction intervals VI curve |
| url | https://doi.org/10.1049/ell2.13147 |
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