Giulio Fedi

Determination of an optimum set of testable components in the fault diagnosis of analog linear circuits

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.

Finding ambiguity groups in low testability analog circuits

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. A Matlab program that implements this method was integrated with a symbolic analysis 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.

On the application of symbolic techniques to the multiple fault location in low testability analog circuits

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 is 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.

A new symbolic method for analog circuit testability evaluation

Testability is a very useful concept in the field of circuit testing and fault diagnosis and can be defined as a measure of the effectiveness of a selected test point set. A very efficient approach for automated testability evaluation of analog circuits is based on the use of symbolic techniques. Different algorithms relying on the symbolic approach have been presented in the past by the authors and in this work noteworthy improvements on these algorithms are proposed. The new theoretical approach and the description of the subsequent algorithm that optimizes the testability evaluation from a computational point of view are presented. As a result, in the computer implementation the roundoff errors are completely eliminated and the computing speed is increased. The program which implements this new algorithm is also presented.

Profiled corrugated circular horns analysis and synthesis via an artificial neural network

In many reflector and lens antennas profiled corrugated circular horns constitute one of the best feed solution thanks to their polarization purity and small size. In this paper, a method for the design of these feeds by using an artificial neural network (ANN) approach is described. The results obtained with such an approach are investigated both for the analysis and for the synthesis problem, and compared with the standard methods. This unconventional solution gives a good level of accuracy and shorter processing times especially for the synthesis problem, where there is still a lack of affordable and fully automated procedures.

A new symbolic approach for testability measurement of analog networks

A new approach for testability measurement of analog networks is presented. It is based on the use of symbolic techniques, that allow us to realize very simple testability evaluation algorithms. The new method presents noteworthy advantages from a computational point of view with respect to previous symbolic techniques of testability measurement developed by the authors in the past. In fact it does not require the computation of the sensitivities of the network functions, but it is based only on the study of the network function symbolic coefficients. A new theorem has been proved and the subsequent new algorithm permits to completely eliminate the roundoff errors and increase the computing speed. A brief introduction to the program which implements this new algorithm is also presented.

A neural architecture for the parameter extraction of high frequency devices [MMICs]

A novel optimization technique for the parameter identification of microwave monolithic integrated circuits is presented. It is based on a hybrid neural network whose learning process convergence allows the validation of the circuit approximated lumped model. The main feature of such a learning process is that no external desired signal is required and the neural network can be considered of the unsupervised type. Furthermore, the neural network output represents the lumped circuit parameter estimation.

A symbolic approach to the fault location in analog circuits

The increased complexity of electronic circuits due to technological improvement has caused the need of always more sophisticated testing and fault diagnosis methodologies. However, while for digital systems these methodologies have now reached full automation, for analog and mixed signal systems there is a lack of efficient and simple methods in this field. The aim of this work is to present a completely new methodology for the parametric fault diagnosis of linear analog circuits. The new method, which is based on the k-fault diagnosis hypothesis and takes into account tolerances and measurement errors, has been fully automated. The automatic fault diagnosis system has been developed by exploiting symbolic techniques, which permit a significant reduction in the computational complexity.

Comments on "Linear circuit fault diagnosis using neuromorphic analyzers"

For the original paper see ibid., vol. 44, no. 3, p. 188-96 (1997). In the aforementioned 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 commenters state that the proposed method does not take into account the testability analysis of the circuit under test. They point out that 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 their 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. Here they discuss this possibility.

FEM-Trained Artificial Neural Networks for the Analysis and Design of Cylindrical Posts in a Rectangular Waveguide

The analysis and design of microwave filters obtained by the insertion of cylindrical posts in a rectangular waveguide is in this paper implemented by a neural network approach. The neural architecture is able to give an accurate description of the filtering device behaviour in almost real time, whereas the full wave simulator would take several minutes. This kind of approach is suitable to analyse a cascade of multiple posts and to provide a solution for the synthesis of such a filtering device.