Analog Circuit Fault Diagnosis Using AdaBoost with SVM-Based Component Classifiers

2012 ◽  
Vol 591-593 ◽  
pp. 1414-1417 ◽  
Author(s):  
Bao Yu Dong ◽  
Guang Ren
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Bandpass Filter ◽  
Simulation Experiment ◽  
Wavelet Packet ◽  
Principal Component ◽  
Monte Carlo Analysis ◽  
Tent Map ◽  
Novel Method ◽  
Circuit Fault Diagnosis

This paper presents a novel method of analog circuit fault diagnosis using AdaBoost with SVM-based component classifiers. We use binary-SVMs of o-a-r SVM as weak classifiers and design appropriate structure of SVM ensemble. Tent map is used to adjust parameters of SVM component classifiers for maintaining the diversity of weak classifiers. In simulation experiment, we use Monte-carlo analysis for 40kHz Sallen-Key bandpass filter and get transient response of thirteen faults. We extract feature vector by db3 wavelet packet transform and principal component analysis (PCA), and diagnose circuit faults by different methods. Simulation results show that the proposed method has the higher classification accuracy compared with other SVM methods. The generalization performance of ensemble method is good. It is suitable for practical use

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.

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.

Analog Circuit Intelligent Fault Diagnosis Based on PCA and OAOSVM

2012 ◽  
Vol 468-471 ◽  
pp. 802-806 ◽  
Author(s):  
Ke Guo ◽  
Yi Zhu ◽  
Ye San
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Principal Component ◽  
Electronic Systems ◽  
Analysis Technique ◽  
Fault Features ◽  
Major Fault ◽  
Circuit Fault Diagnosis

Fault diagnosis of analog circuits is essential for guaranteeing the reliability and maintainability of electronic systems. Analog circuit fault diagnosis can be regarded as a pattern recognition issue and addressed by one-against-one SVM. In order to obtain a good SVM-based fault classifier, the principal component analysis technique is adopted to capture the major fault features. The extracted fault features are then used as the inputs of SVM to solve fault diagnosis problem. The effectiveness of the proposed method is verified by the experimental results.

Analog Circuit Fault Diagnosis Based on Principal Component Analysis of Pretreatment and Particle Swarm Hybrid Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 809-812
Author(s):  
Guo Huang ◽  
Bao Ru Han
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Hybrid Neural Network ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

With the rapid development of electronic technology, the system reliability and economic requirements of the importance of the analog circuit fault diagnosis has become increasingly prominent. Aiming at the shortcomings of traditional diagnosis method, the paper presents an analog circuit fault diagnosis method based on principal component analysis of pretreatment and particle swarm hybrid neural network. The method adopts hybrid algorithm to adjust the network weights and thresholds to avoid falling into the local minimum value, which uses principal component pretreatment effectively reduce the complexity of calculation. Simulation results show that the diagnostic method can be used for tolerance analog circuit fault diagnosis, has better application prospect.

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