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 Application of DBN and GWO-SVM in analog circuit fault diagnosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiyuan Su ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Experimental Results ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Highpass Filter ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Factor C

AbstractFor large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

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 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.

GA Optimized Binary Tree SVM for Analog Circuit Fault Diagnosis

2012 ◽  
Vol 235 ◽  
pp. 423-427 ◽  
Author(s):  
Bao Yu Dong ◽  
Guang Ren
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Binary Tree ◽  
Analog Circuit ◽  
Wavelet Packet ◽  
Principal Component ◽  
Monte Carlo Analysis ◽  
Support Vector ◽  
Roulette Wheel Selection ◽  
Circuit Fault Diagnosis

This paper presents a novel method of analog circuit fault diagnosis based on genetic algorithm (GA) optimized binary tree support vector machine (SVM). The real-valued coding genetic algorithm is used to optimize the binary tree structure. In optimization algorithm, we use roulette wheel selection operator, partially mapped crossover operator, inversion mutation operator. In simulation experiment, we use Monte-carlo analysis for 40kHz Sallen-Key bandpass filter and get transient response of ten faults. Then we extract feature vector by db3 wavelet packet transform and principal component analysis (PCA), and diagnose circuit faults by different SVM methods. Experiment results show the proposed method has the better classification accuracy than one-against-one (o-a-o), one-against-rest (o-a-r), Directed Acyclic Graph SVM (DAGSVM) and binary tree SVM (BT-SVM). It is suitable for practical use.

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