Practical Analog Circuit Diagnosis Based on Fault Features with Minimum Ambiguities

As numerous faults exist in practical analog circuits, new challenges arise in the field of diagnosis with large-scale target faults as well as fault features. To address this issue, firstly, an ambiguity model is built to measure the distinguishability between two faults. Then, the optimal fault features are obtained by analyzing the response curves of the circuit under test (CUT) to minimize the ambiguities among the faults. Finally, comparisons are made among three classification methods, including the maximum likelihood classifier (MLC), artificial neural networks (ANNs) and support vector machine (SVM), to demonstrate their own diagnostic abilities for practical use. Two examples are illustrated, and taking advantage of an automated implementation framework, 92 faults in total are examined in the second example. The experimental results show that good diagnostic performances can be obtained with the proposed method. However, when a practical case is encountered, the ANNs method may fail due to its high time and space complexity, while the MLC and SVM methods are still applicable.