A collection of fuzzy logic-based tools for the automated design, modelling and test of analog circuits

We have developed a collection of tools for the design, modeling, and test of analog circuits. Sharing a common fuzzy-logic based framework, the tools are part of FASY (Fuzzy-Logic-Based Analog Synthesis), an analog design package developed at the University of Seville. The first tool uses fuzzy logic for topology selection of analog cells. It follows decision rules directly entered by a human expert or automatically generated from its experience with earlier designs. Second, a performance-modeling tool provides a qualitative description of a circuit's behavior. Alternatively, it can use a learning process to accurately model circuit performance. Finally, an analog testing tool uses a fuzzy-neuron classifier to detect and classify faults in analog circuits

Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea

Construction of an Expert System Based on Fuzzy Logic for Diagnosis of Analog Electronic Circuits

The paper presents construction of the fuzzy logic system to analog circuits soft 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

Measurements, Models, Systems and Design

531 s.

Diagnosis of Frequency Response Analog Circuits using HHO-SVM

Monitoring the system, recognising when a fault has occurred, identifying the kind of defect and where it is located are all aspects of fault detection and isolation. To assess whether a problem has arisen inside a certain channel or region of operation, fault detection is used. For many technological processes in the creation of effective and safe advanced supervision systems, fault detection and diagnosis have grown in significance. This article's main goal is to increase the accuracy of faults detection in frequency response analogue circuits and execution of work needs to be speed up. For this purpose, two optimization techniques are used. One is grey wolf optimization (GWO) for the process of feature extraction and secondly Harris Hawk optimization (HHO) as classifier optimizer.   the features and optimize the classifier. The Sallen key circuit (SKC) are utilized for processing the input data. The filters like low pass, high pass and bandpass are designed based on SKC and optimized using GWO. Finally, the optimized features obtained from different circuits are fed to support vector machine classifier to identify the fault accuracy in the circuit. The SVM classifier is optimized using HHO to achieve best accurate output. The suggested technique with a low-dimensional feature optimisation and optimised classifier performed better than the prior methods according to simulation findings, and computing time was also greatly minimised

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd