scholarly journals A Naive-Bayes-Based Fault Diagnosis Approach for Analog Circuit by Using Image-Oriented Feature Extraction and Selection Technique

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5065-5079 ◽  
Author(s):  
Wei He ◽  
Yigang He ◽  
Bing Li ◽  
Chaolong Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Selection Technique ◽  
Feature Extraction And Selection ◽  
Diagnosis Approach

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.

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.

Sign in / Sign up

Export Citation Format

Share Document