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An efficient approach for ambiguity group determination in low-testability analog linear circuits is presented. It is based on the use of the singular-value decomposition of the testability matrix of the circuit under test, and permits to determine canonical ambiguity groups also in case of circuits of relatively large dimensions. The new approach is characterized by a numerical robustness not present in previous approaches, which give only an estimate of both testability and ambiguity groups. A program implementing the proposed method has been developed by exploiting symbolic analysis techniques. Examples of application of the new approach are considered through the use of this program. Index Terms--Analog system testing, singular-value decomposition (SVD), testability analysis.

A procedure for the determination of an optimum set of testable components in the fault diagnosis of analog linear circuits is presented. The proposed method has its theoretical foundation in the testability concept and in the canonical ambiguity group concept. New considerations relevant to the existence of unique solution in the k-fault diagnosis problem of analog linear circuits are presented, and examples of application of the developed procedure are considered by exploiting a software package based on symbolic analysis techniques. Index Terms - Analog system fault diagnosis, analog system testing, fault location.

A new approach for the multiple fault location in linear analog circuits is proposed. It presents the characteristic of using classical numerical procedures together with symbolic analysis techniques, which result particularly useful in the parametric fault diagnosis field. The proposed approach is based on the k-fault hypothesis and is provided with efficient algorithms for fault location also in the case of low testability circuits. The developed algorithms have been used for realizing a software package prototype which implements a fully automated system for the fault location in linear analog circuits of moderate size. Index Terms - Analog circuits, fault diagnosis, symbolic techniques.

In a recent paper, Spina and Upadhyaya presented a method for the fault diagnosis of analog linear circuits. The method, which is based on a white noise generator and an artificial neural network for response analysis, has been applied to circuits of reasonable dimensions, taking into account the effect of the component tolerances. However, the proposed method does not take into account the testability analysis of the circuit under test. Research on testability analysis of linear circuits has been developed by several authors in the last 20 years, and algorithms and programs for testability evaluation have been presented in several publications. It is our opinion that the testability analysis concept could be useful in the approach proposed by Spina and Upadhyaya to improve the quality of the results even further. In this brief, we discuss this possibility. Index Terms - Analog system fault diagnosis, analog system testing, fault location.

This paper discusses a numerically efficient approach to identify complex ambiguity groups for the purpose of analog fault diagnosis in low-testability circuits. The approach presented uses a numerically efficient QR factorization technique applied to the testability matrix. Various ambiguity groups are identified. This helps to find unique solution of fault diagnosis equations or identifies which groups of components can be uniquely determined. This work extends results reported earlier-in literature, where QR factorization was used in low-testability circuits, significantly increasing efficiency to determine ambiguity groups. Matlab program that implements this method was integrated with a symbolic analysts program that generates test equations. The method is illustrated on two low-testability electronic circuits. Finally, method efficiency is tested on larger electronic circuits with several hundred tested parameters. Index Terms--Ambiguity groups, analog system testing, low testability, QR factorization.