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

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. We present an ensemble neural network approach for enhancing the accuracy of circuit board fault detection by addressing the inherent uncertainty in the VI curve. The proposed approach integrates a neural network-based prediction interval with ensemble techniques, formulating a multi-objective loss function that combines curve fitting and prediction interval to optimize current-voltage relationships. The ensemble approach is employed to mitigate model uncertainty, resulting in improved fault detection accuracy, as demonstrated through experimental validation on circuit boards.