The Application of HIWO–SVM in Analog Circuit Fault Diagnosis

2015 ◽  
Vol 29 (08) ◽  
pp. 1550019 ◽  
Author(s):  
Hongzhi Hu ◽  
Shulin Tian ◽  
Qing Guo ◽  
Aijia Ouyang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
The Other ◽  
Support Vector ◽  
De Algorithm ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Diagnosis Model ◽  
Test Points ◽  
Circuit Fault Diagnosis

The paper proposes a fault diagnosis model based on the HIWO–SVM algorithm given the fact that the basic support vector machines (SVM) cannot solve effectively the problem of fault diagnosis in analog circuit. First of all, the wavelet package technique is adopted for extracting the information of the faults from the test points in the analog circuit. The differential evolution (DE) algorithm is then integrated with the purpose of improving the performance of the basic IWO algorithm, i.e. a hybrid IWO (HIWO) algorithm. The HIWO algorithm is further used to optimize the parameters of SVM in order to avoid the randomness of the parameter selection, thereby improving the diagnosis precision and robustness. The experimental results on a filter circuit show that the method is more effective and reliable than the other methods for fault diagnosis.

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.

Phasor Analysis Based Fault Modeling and Fault Diagnosis Methods for Linear Analog Circuits

2014 ◽  
Vol 981 ◽  
pp. 3-10 ◽  
Author(s):  
Yuan Gao ◽  
Cheng Lin Yang ◽  
Shu Lin Tian
Keyword(s):  
Fault Diagnosis ◽  
Data Base ◽  
Analog Circuit ◽  
Feature Selection Method ◽  
Fault Modeling ◽  
Modeling Method ◽  
Support Vector ◽  
Linear Analog ◽  
Circuit Fault Diagnosis ◽  
Phasor Analysis

Soft fault diagnosis and tolerance are two challenging problems in linear analog circuit fault diagnosis. To solve these problems, a phasor analysis based fault modeling method and its theoretical proof are presented at first. Second, to form fault feature data base, the differential voltage phasor ratio (DVPR) is decomposed into real and imaginary parts. Optimal feature selection method and testability analysis method are used to determine the optimal fault feature data base. Statistical experiments prove that the proposed fault modeling method can improve the fault diagnosis robustness. Then, Multi-class support vector machine (SVM) classifiers are used for fault diagnosis. The effectiveness of the proposed approaches is verified by both simulated and experimental results.

scholarly journals A Novel Analog Circuit Fault Diagnosis Approach

2021 ◽  
Vol 14 ◽  
Author(s):  
Chaolong Zhang ◽  
Lingling Ye ◽  
Jing Wu ◽  
Bo Zhang ◽  
Ningguang Yao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extraction Method ◽  
High Performance ◽  
Analog Circuit ◽  
Health Management ◽  
Extraction Methods ◽  
Wavelet Coefficients ◽  
Core Components ◽  
Diagnosis Model ◽  
Circuit Fault Diagnosis

Background: Correct classifying of analog circuit faults is helpful in the health management of the circuit. It is difficult to be implemented because of the lack of proper feature extraction methods and accurate fault diagnosis models. Objective: T-SNE based core components extraction method and PSO-ELM-based fault diagnosis model are presented to improve the diagnostic accuracy of analog circuit fault diagnosis. Method: Firstly, circuit output signals are collected, and they are transformed to wavelet coefficients. Then, the high-dimensional wavelet coefficients are processed by t-SNE to generate low-dimensional core components as features. The extreme learning machine (ELM) based diagnosing model is constructed by using the features, and the key parameters of ELM are optimized by using particle swarm optimization (PSO) algorithm. Finally, the constructed PSO-ELM diagnosis model is employed to identify different analog circuit faults. Results: Leapfrog filter circuit and three-phase bridge circuit fault diagnosis experiments are implemented to demonstrate the proposed t-SNE based features extraction method and PSO-ELM based fault diagnosis model. Also, comparisons are performed to verify the high performance of proposed fault diagnosis methods. Conclusion: The proposed t-SNE based core components extraction method and PSO-ELM diagnosis model are effective to improve the fault diagnosis accuracy of the analog circuit.

scholarly journals Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

2018 ◽  
Vol 173 ◽  
pp. 03090
Author(s):  
WANG Ying-chen ◽  
DUAN Xiu-sheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Network Application ◽  
Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

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