基于选择性集成神经网络的电路板故障智能诊断

Intelligent Diagnosis of Circuit Board Failure Based on Selective Integrated-neural-network

针对基于红外图像的电路板故障诊断准确率较低、检测灵敏度差等缺陷,分析了基于神经网络的智能诊断方法。该方法结合多分类器转化为二分类器思想,设计了一种基于BP神经网络的集成神经网络诊断模型,并且对于同一类故障采取范围化样本进行训练,每组被测故障数据根据特征阈值选择相关几个子网络进行诊断。最后利用Matlab软件进行实例仿真和测试。结果表明:该网络对于电路板多故障模式的识别准确率较高,检测灵敏度可以提高1.74倍,而预测误差可以降低到原来的17.6%,为电路板故障诊断的实用化提供了理论依据。

In view of limitations of the circuit board fault diagnosis technology on infrared images, in thts paper, the intelligent diagnosis method is analyzed. In the method of neural networks, the multiple classifiers are turned into a dichotomous thinking, and an integrated neural network diagnosis model is designed based on BP neural network. For the same type of faults, samples within a range are trained in the net- work, and for each group of the measured fault data and the several sub-threshold selected, the diagnosis is made according to the characteristics. Finally, the living examples are simulated and tested by using MATLAB. The results show that the recognition accuracy is improved,the detection sensitivity can be increased by 1.74 times, and the prediction error is decreased to 17.6% of the original prediction error of the more-fault-mode network. This provides a theoretical basis for the practical circuit fault diagnosis.