一种大规模模拟电路快速故障诊断新方法

针对传统大规模模拟电路故障诊断方法在多故障条件下的故障定位过程复杂、测前工作量大等问题, 提出了一种新的故障诊断方法——成组撕裂法。将大规模模拟电路按照拓扑特性和成组撕裂准则进行撕裂, 得到低维度的故障特征向量; 基于模式识别思想, 选用具有高度并行分类能力的神经网络作为分类器, 隐含层传递激发函数选择具有快速收敛特性的小波函数。经仿真验证该方法能实现故障特征向量的快速分类并得出故障诊断结果。与目前已有的互校验（multiple-test-condition, MTC）和交叉撕裂搜索法相比, 该方法有测前工作量小、诊断次数和计算量少、对多故障检测能力和工程实践性强等特点。

New approach for fast fault diagnosis of large-scale analogue circuits

To solve the problems of complex fault location process and heavy workload before test using traditional large-scale circuit fault diagnosis methods in the multi-fault conditions, this paper presented a new approach for fault diagnosis using group decomposition. Low dimensional fault eigenvectors could be acquired by decomposing the large-scale analogue circuit in accordance with its topological property and group decomposition criteria. Based on ideas of pattern recognition, neural network which had highly parallel classify capability was selected as classifier and wavelet function which had fast convergence property was selected as hidden layer's transfer excitation function. The simulation results show that this method can achieve fast fault feature vector classification and fault diagnosis results. Comparing with the existing multiple-test-condition (MTC) method and intersection tearing method, this approach has smaller amount of work before test, fewer diagnostic times, less computation, better capability of diagnosing multiple faults and stronger engineering practicality.