scholarly journals Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 604 ◽  
Author(s):  
Wei He ◽  
Yigang He ◽  
Bing Li ◽  
Chaolong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Support Vector ◽  
Svm Classifier ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Circuit Fault Diagnosis ◽  
Value Decomposition ◽  
Environment Noise ◽  
Cross Wavelet

In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits’ defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

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.

Analog Circuit Fault Diagnosis Based on Support Vector Machine

2014 ◽  
Vol 1055 ◽  
pp. 99-102
Author(s):  
Jun Shi ◽  
Tong Shu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Wavelet Packet ◽  
Parameter Determination ◽  
Support Vector ◽  
Svm Classifier ◽  
Wavelet Packet Decomposition ◽  
Classification Problems ◽  
Circuit Fault Diagnosis

Analog circuit fault diagnosis is essentially a multiple state pattern classification problems. The traditional support vector machine classifier is for binary classification problems. In more than three kinds of commonly used class promotion model. This paper adopted the decision directed acyclic graph of multi-value classification algorithm. Multi-fault SVM classifier model is established. And the kernel function selection and nuclear parameter determination method were studied. Based on this model, support vector machine is used for analog circuit fault diagnosis given the basic idea and implementation steps. In analog circuit fault feature extraction technology, the effective sample point voltage amplitude response signal as well as the fault characteristic samples are extracted by wavelet packet decomposition of the energy spectrum method to extract the signal fault characteristics as the fault samples, formed based on effective sampling points. The SVM classifier is based on wavelet packet decomposition and the SVM classifier two methods of analog circuit 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.

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 Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Songrong Luo ◽  
Junsheng Cheng ◽  
HungLinh Ao
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Values ◽  
Value Decomposition

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

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.

Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method

2012 ◽  
Vol 263-266 ◽  
pp. 108-113 ◽  
Author(s):  
Jing Yuan Tang ◽  
Jian Ming Chen ◽  
Cai Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Accurate Method ◽  
Svm Classifier ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Detrended Fluctuation ◽  
Nonlinear Analog

This paper presents a fault diagnosis method for nonlinear analog circuit based on multifractal detrended fluctuation analysis (MFDFA) method. The MFDFA method is applied to analysis fault signal and extracts the multifractal features from the raw signal. The selected features are given to SVM classifier for further classification. The data required to develop the classifier are generated by simulating various faults using Pspice software. The simulation results show that the proposed method provides a robust and accurate method for nonlinear circuit fault diagnosis.

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