Fault Diagnosis for Nonlinear Analog Circuit Based on Harmonic Decomposition and Coherent Measurement

2010 ◽  
Vol 40-41 ◽  
pp. 245-251
Author(s):  
Chen Xi Xu ◽  
Xian Zhi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear Dynamic ◽  
Analog Circuit ◽  
Fault Current ◽  
Nonlinear Circuit ◽  
Weakly Nonlinear ◽  
Harmonic Decomposition ◽  
Nonlinear Analog ◽  
Diagnosis Approach

This paper, a fault diagnosis approach for nonlinear dynamic circuit is presented based on harmonic decomposition and coherent measurement. According to the separability of Volterra spectral in weakly nonlinear circuit under AM stimulation, the Volterra response can decompose into linear sub-circuits based on ARMA harmonic decomposition firstly. In linear sub-circuits, the mapping relationship between the fault characteristics and fault states will become more clearly. Then, the dynamic linear sub-circuit is transformed into static circuit by coherent measurement, and the diagnosis equation is created with circuit node-equation. The fault diagnosis can be implemented by calculating the nodes’ fault-current. Finally, with an example to illuminate the proposed is available.

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 Fault Diagnosis of Nonlinear Analog Circuit Based on Generalized Frequency Response Function and LSSVM Classifier Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jialiang Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Response ◽  
Frequency Response Function ◽  
Analog Circuit ◽  
Least Square ◽  
Classifier Fusion ◽  
Support Vector ◽  
Response Functions ◽  
Nonlinear Analog

For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.

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.

Nonlinear Analog Circuit Fault Diagnosis Based on MF-DFA Method

2010 ◽  
Vol 22 (5) ◽  
pp. 852-857 ◽  
Author(s):  
Jingyuan Tang ◽  
Yibing Shi ◽  
Wei Zhang ◽  
Longfu Zhou
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Circuit Fault Diagnosis ◽  
Nonlinear Analog
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