摘要
大规模的数模混合电路所含故障模式众多,电路故障状态复杂,且易发生传播,因而电路故障诊断难度较大。针对大规模电路发生故障时存在故障传播的问题,提出一种基于故障传播的模块化BP神经网络(MBPFP)故障诊断方法。首先,在电路模块划分的基础上分析子电路间的故障传播,并将故障源和故障传播源"模块化";然后,通过子电路的异常检测模型进行一级定位,缩小故障原因集合,确定故障模块;最后,利用目标模块的BP神经网络模型进行二级定位,实现故障诊断并识别故障模式。与传统BP神经网络等方法进行比较的实验结果表明,MBPFP故障诊断方法具有较高的故障覆盖率,在定位准确率方面提高了至少8个百分点,其性能优于传统BP神经网络等方法。
It is difficult to diagnose the faults of large-scale digital-analog hybrid circuit because it has numerous fault modes, the circuit failure status is complex and can be propagated easily. To solve these problems, a new failure diagnosis method, namely Modularized Back Propagation( BP) neural network based on Fault Propagation( MBPFP), was proposed.Firstly, fault propagation between subcircuits was analyzed on the basis of circuit module division, and failure source and transmission source were modularized. Secondly, the set of fault causes was narrowed and the fault module was determined by the anomaly detection model of subcircuit in 1-order positioning. Finally, the fault location was realized and the fault mode was identified by the BP neural network of target module in 2-order positioning. The experimental results show that compared with the traditional BP neural network method, the proposed MBPFP method has a high fault coverage and the accuracy is improved by at least 8 percentage points, which is outperforms the traditional method based on BP neural network.
出处
《计算机应用》
CSCD
北大核心
2018年第2期602-609,共8页
journal of Computer Applications
关键词
大规模数模混合电路
故障诊断
故障传播
BP神经网络
异常检测模型
large-scale digital-analog hybrid circuit
fault diagnosis
fault propagation
Back Propagation(BP) neural network
anomaly detection model
