The Study about Feature Extraction of Analog Circuit Fault Diagnosis Based on Annealing Genetic Hybrid Algorithm

2013 ◽  
pp. 1133-1139
Author(s):  
Lin Hai-Jun ◽  
Wan Si-Bo ◽  
Zhong Hui ◽  
Wang Qi-Gao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hybrid Algorithm ◽  
Analog Circuit ◽  
Circuit Fault Diagnosis

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1049-1052
Author(s):  
Bao Ru Han ◽  
Jing Bing Li
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Algorithm ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Bp Algorithm ◽  
Particle Swarm Algorithm ◽  
Inertia Weight ◽  
High Convergence Rate ◽  
Swarm Algorithm ◽  
Circuit Fault Diagnosis

Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation results show that this diagnostic method can be used for tolerance analog circuit fault diagnosis, with a high convergence rate and diagnostic accuracy.

scholarly journals Fault Diagnosis of an Analog Circuit Based on Hierarchical DVS

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1901
Author(s):  
Yong Deng ◽  
Yuhao Zhou
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Analog Circuit ◽  
Nearest Neighbor ◽  
Volterra Series ◽  
System Modeling ◽  
Information Criterion ◽  
Feature Extraction Method ◽  
Circuit Fault Diagnosis

Analog circuit fault diagnosis technology is widely used in the diagnosis of various electronic devices. The basic strategy is to extract circuit fault characteristics and then to use a clustering algorithm for diagnosis. The discrete Volterra series (DVS) is a common feature extraction method; however, it is difficult to calculate its parameters. To solve the problem of feature extraction in fault diagnosis, we propose an improved hierarchical Levenberg–Marquardt (LM)–DVS algorithm (IDVS). First, the DVS is simplified on the basis of the hierarchical symmetry of the memory parameters, the LM strategy is used to optimize the coefficients, and a Bayesian information criterion based on the symmetry of entropy is introduced for order selection. Finally, we propose a fault diagnosis method by combining the improved DVS algorithm and a condensed nearest neighbor algorithm (CNN) (i.e., the IDVS–CNN method). A simulation experiment was conducted to verify the feature extraction and fault diagnosis ability of the IDVS–CNN. The results show that the proposed method outperforms conventional methods in terms of the macro and micro F1 scores (0.903 and 0.894, respectively), which is conducive to the efficient application of fault diagnosis. In conclusion, the improved method in this study is helpful to simplify the calculation of the DVS parameters of circuit faults in analog electronic systems, and provides new insights for the prospective application of circuit fault diagnosis, system modeling, and pattern recognition.

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.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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