scholarly journals A Tool for Single-Fault Diagnosis in Linear Analog Circuits with Tolerance Using the T-Vector Approach

VLSI Design ◽  
2008 ◽  
Vol 2008 ◽  
pp. 1-8
Author(s):  
José A. Soares Augusto ◽  
Carlos Beltrán Almeida

In previous works of these authors, a technique for doing single-fault diagnosis in linear analog circuits was developed. Under certain conditions, one of them assuming nominal values for the circuit parameters, it was shown that only two measurements taken on two selected circuit nodes, at a single frequency, were needed to detect and diagnose any parametric fault. In this paper, the practical value of the technique is improved by extending the application to the diagnosis of faults in circuits with parameters subject to tolerance. With this in mind, single parametric faults with several strengths are randomly injected in the circuit under study and, afterwards, these faults are diagnosed (or the diagnosis fails). Results are reported on a simple active filter. Conclusions are drawn about the robustness and effectiveness of the technique.

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 714-722 ◽  
Author(s):  
Michał Tadeusiewicz ◽  
Stanisław Hałgas

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 228 ◽  
Author(s):  
Ling Wang ◽  
Dongfang Zhou ◽  
Hui Tian ◽  
Hao Zhang ◽  
Wei Zhang

The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.


2015 ◽  
Vol 61 (1) ◽  
pp. 77-82 ◽  
Author(s):  
Damian E. Grzechca

Abstract The paper presents construction of the fuzzy logic system to analog circuits parametric fault diagnosis. The classical dictionary construction is replaced by fuzzy rule system. The first part refers to analog fault diagnosis, its techniques, approaches and goals. It clarifies common strategy and define differences between detecting, locating and identifying a fault in analog electronic circuit. The second part is focused on a creation of fuzzy rule expert system with use of sensitivity functions and known circuit topology. To detect, locate and identify a faulty element in a circuit the sensitivity matrix is used. The advantage of the method is its utilization in all, AC, DC and time domain. The fuzzy system, like the classical fault dictionary, can detect and locate single catastrophic faults and, on the contrary to the classical one, it also detects and locates parametric faults. Moreover, it allows identification of these faults, such that sign of the faulty parameter deviation is designated. The method has deterministic character as well as it can be applied on the verification and production stage


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