Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve

Author:

Pan Qingguo1,Zhao Yan2,Zhao Zheng2,Lin Peng2ORCID

Affiliation:

1. Wuhu State‐Owned Factory of Machining Wuhu Anhui China

2. Artificial Intelligence Institute Hangzhou Dianzi University Hangzhou China

Abstract

AbstractThe 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.

Funder

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Reference7 articles.

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