Analog circuit fault diagnosis combing wavelet packet with higher order statistics

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

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GA Optimized Binary Tree SVM for Analog Circuit Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.235.423 ◽

2012 ◽

Vol 235 ◽

pp. 423-427 ◽

Cited By ~ 1

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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Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings

Journal of Vibration and Control ◽

10.1177/1077546308091214 ◽

2008 ◽

Vol 14 (11) ◽

pp. 1691-1709 ◽

Cited By ~ 64

Author(s):

Fucai Li ◽

Guang Meng ◽

Lin Ye ◽

Peng Chen

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Order Statistics ◽

Higher Order ◽

Higher Order Statistics ◽

Rolling Element Bearings ◽

Rolling Element

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Analog Circuit Fault Diagnosis Method Based on Preferred Wavelet Packet and ELM

Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) ◽

10.2991/eame-17.2017.1 ◽

2017 ◽

Author(s):

Haitao Shi ◽

Qide Tan ◽

Chenggang Li ◽

Xiangyu Lv

Keyword(s):

Fault Diagnosis ◽

Analog Circuit ◽

Wavelet Packet ◽

Diagnosis Method ◽

Circuit Fault Diagnosis

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Analog Circuit Fault Diagnosis Based on Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1055.99 ◽

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.

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