Neural networks in automatic testing of diode protection circuits

IEEE Automatic Testing Conference.The Systems Readiness Technology Conference. Automatic Testing in the Next Decade and the 21st Century. Conference Record. Pub Date : 1989-09-25 DOI:10.1109/AUTEST.1989.81118

L. Allred

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引用次数: 2

Abstract

Limiting Zener diode circuits are often used in ground support equipment for the Minuteman Missile. These circuits protect sensitive transistor and resistor components from electrical surges. Data were collected for 110 waveforms for a combination of good circuits and the most frequently encountered failure modes, including shorted diodes, open diodes and bad amplifiers. The data were then used to train a neural network pattern recognition system to see if neural network technology could correctly identify good versus bad protection circuits. When trained using all of the diodes, the neural network was able to identify correctly all of the circuits and associated failure models. To validate the neural network model, a subset of 59 samples was randomly selected for training of the neural network, and the remaining 51 samples were used for testing. In both instances, the network did an excellent job (100%) of identifying failure.<>

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神经网络在二极管保护电路自动测试中的应用

限制齐纳二极管电路常用于民兵导弹的地面保障设备。这些电路保护敏感的晶体管和电阻元件免受电涌的影响。收集了110种波形的数据，包括良好电路和最常见的故障模式，包括短路二极管，打开二极管和坏放大器。然后，这些数据被用来训练神经网络模式识别系统，看看神经网络技术是否能正确识别好的和坏的保护电路。当使用所有二极管进行训练时，神经网络能够正确识别所有电路和相关的故障模型。为了验证神经网络模型，随机选取59个样本子集进行神经网络训练，剩余的51个样本进行测试。在这两个例子中，网络在识别故障方面都做得很好(100%)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE Automatic Testing Conference.The Systems Readiness Technology Conference. Automatic Testing in the Next Decade and the 21st Century. Conference Record.

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