scholarly journals Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yuehai Wang ◽  
Yongzheng Yan ◽  
Qinyong Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Integrated Circuits ◽  
Analog Circuits ◽  
Integrated Circuit ◽  
Analog Circuit ◽  
Pass Filter ◽  
Op Amp ◽  
Biquad Filter ◽  
Feature Extraction And Selection

Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In this paper, based on wavelet analysis, we will study the problems of mother wavelets selection, number of decomposition levels, and candidate coefficients selection by using a four-op-amp biquad filter circuit. After conducting several comparative experiments, some general guidelines for feature extraction for this type of analog circuits fault diagnosis are derived.

scholarly journals Evolutionary algorithms for global parametric fault diagnosis in analogue integrated circuits

2012 ◽  
Vol 60 (1) ◽  
pp. 133-142 ◽  
Author(s):  
P. Jantos ◽  
D. Grzechca ◽  
J. Rutkowski
Keyword(s):  
Fault Diagnosis ◽  
Integrated Circuits ◽  
Evolutionary Algorithms ◽  
Integrated Circuit ◽  
Gene Expression Programming ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Parametric Fault ◽  
Diagnosis Method ◽  
Analogue Integrated Circuits

Evolutionary algorithms for global parametric fault diagnosis in analogue integrated circuitsAn evolutionary method for analogue integrated circuits diagnosis is presented in this paper. The method allows for global parametric faults localization at the prototype stage of life of an analogue integrated circuit. The presented method is based on the circuit under test response base and the advanced features classification. A classifier is built with the use of evolutionary algorithms, such as differential evolution and gene expression programming. As the proposed diagnosis method might be applied at the production phase there is a method for shortening the diagnosis time suggested. An evolutionary approach has been verified with the use of several exemplary circuits - an oscillator, a band-pass filter and two operational amplifiers. A comparison of the presented algorithm and two classical methods - the linear classifier and the nearest neighborhood method - proves that the heuristic approach allows for acquiring significantly better results.

Analog Circuit Failure Analysis Using Time-Resolved Emission

2005 ◽  
Author(s):  
B.J. Cain ◽  
G.L. Woods ◽  
A. Syed ◽  
R. Herlein ◽  
Toshihiro Nomura
Keyword(s):  
Analog Circuits ◽  
Integrated Circuit ◽  
Analog Circuit ◽  
Photon Emission ◽  
Nonlinear Process ◽  
Time Resolved ◽  
Non Invasive ◽  
Highly Nonlinear ◽  
Excellent Spatial Resolution ◽  
5 Ghz

Abstract Time-Resolved Emission (TRE) is a popular technique for non-invasive acquisition of time-domain waveforms from active nodes through the backside of an integrated circuit. [1] State-of-the art TRE systems offer high bandwidths (> 5 GHz), excellent spatial resolution (0.25um), and complete visibility of all nodes on the chip. TRE waveforms are typically used for detecting incorrect signal levels, race conditions, and/or timing faults with resolution of a few ps. However, extracting the exact voltage behavior from a TRE waveform is usually difficult because dynamic photon emission is a highly nonlinear process. This has limited the perceived utility of TRE in diagnosing analog circuits. In this paper, we demonstrate extraction of voltage waveforms in passing and failing conditions from a small-swing, differential logic circuit. The voltage waveforms obtained were crucial in corroborating a theory for some failures inside an 0.18um ASIC.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

scholarly journals Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Peng-yuan Liu ◽  
Bing Li ◽  
Cui-e Han ◽  
Feng Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Operating Conditions ◽  
Nsga Ii ◽  
Selection Scheme ◽  
Time Frequency ◽  
Feature Extraction And Selection

A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. In the phase of feature extraction, theStransform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions. Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations. In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis. Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis.

Fault Diagnosis of Analog Circuits with Tolerance Based on Simulation-before-Test and Distance Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 156-160
Author(s):  
Yong Jie Zhang ◽  
Jian Jun Hou ◽  
Liang Huang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Euclidean Distance ◽  
Analog Circuit ◽  
Practical Method ◽  
Fault Dictionary ◽  
Advantages And Disadvantages ◽  
Distance Methods

Fault dictionary is the most practical method of fault diagnosis in analog circuit. Before analog circuit with tolerance is diagnosed, circuit is simulated by computer. Typical parameters of every state are used to build fault dictionary. Distance algorithm is used to calculate the similarity between current circuit and every state of fault dictionary. Analog circuit with tolerance can be diagnosed by the distance. Firstly, the method of simulation-before-test is introduced to build fault dictionary. Secondly, familiar distance algorithm is resumed, such as Euclidean distance. Finally, an example of fault diagnosis of analog circuit with tolerance is provided. In the example, simulation-before-test and distance algorithm are combined to diagnose analog circuit with tolerance. Two distance methods are compared to explain the advantages and disadvantages of the Euclidean distance algorithm.

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