摘要
针对模拟电路板芯片故障界定标准不明确和实现快速、准确分类困难的问题,本文提出了一种基于双元卷积Logistic原子搜索算法(BCL-ASA)优化BP神经网络(BCL-ASA-BP)的故障诊断模型。首先,对电路板芯片不同状态下的温度进行采集和特征提取,并采用欧氏距离对特征进行融合,建立含有芯片故障界定标准的故障特征模型。接着,利用双元卷积Logistic映射初始化原子搜索算法的种群规模和位置,提高收敛速度和精度。然后,通过BCL-ASA优化BP神经网络寻优过程,获得最优权值和阈值。最后,将芯片故障特征模型输入到BCL-ASA-BP神经网络中进行训练和测试,完成电路板芯片故障诊断。实验采用电源电路板进行可靠性分析,结果表明,BCL-ASA-BP对芯片故障综合诊断准确率可达98.35%,较传统BP算法提升13.9%。
Aiming at the problems that the fault definition standard of analog circuit board chip is not clear and it is difficult to realize fast and accurate classification, this paper proposes a fault diagnosis model based on binary convolution logistic atom search algorithm to optimize BP neural network. Firstly, the temperature of circuit board chip in different states is collected and feature extracted, and the features are fused by Euclidean distance to establish a fault feature model containing chip fault definition criteria. Then, the binary convolution logistic map is used to initialize the population size and location of the atomic search algorithm to improve the convergence speed and accuracy. Then, the optimization process of BP neural network is optimized by BCL-ASA to obtain the optimal weight and threshold. Finally, input the chip fault characteristic model into the BCL-ASA-BP neural network for training and testing to complete the circuit board chip fault diagnosis. The experiment uses the power supply circuit board for reliability analysis, and the results show that the accuracy of BCL-ASA-BP′s comprehensive diagnosis of chip faults reaches 98.35%, which is 13.9% higher than the traditional BP algorithm.
作者
王力
刘学朋
张亦弛
Wang Li;Liu Xuepeng;Zhang Yichi(School of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China;Deep Maintenance Laboratory of Airborne Electronie System,Civil Aviation University of China,Tianjin 300300,China)
出处
《电子测量技术》
北大核心
2022年第14期164-171,共8页
Electronic Measurement Technology
基金
国家自然科学基金委员会与中国民用航空局联合资助基金(U1733119)
中央高校基本业务费项目(3122017107)
基于红外技术与数据驱动的机载电路板卡故障诊断与预测研究项目(2021YJS018)资助。
