Fault diagnosis of analog circuits based on wavelet packets

He, Yigang; Tan, Yanghong; Sun, Yichuang

doi:10.1109/tencon.2004.1414408

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…To construct the input pattern matrices, the MFDFA features are extracted by the MFDFA method to analysize all fault signals. In order to evaluate the effectiveness of the MFDFA features to discriminate different faults, we have applied the MFDFA features to Duffing circuit and the diagnosis results are compared with other features, such as discrete wavelet transform (DWT) features [3] and wavelet packet transform (WPT) features [6], to classify different faults using SVM classifier.…”

Section: Information Technology Applications In Industrymentioning

confidence: 99%

“…However, no mathematical model of the process is required in the pattern recognition methods as the operation of the process is classified by matching the measurement data. Some intelligent classification algorithms, such as artificial networks (ANNs) and support vector machines (SVMs) have been successfully applied to fault diagnosis for analog circuit [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method

Tang¹,

Chen²,

Cai³

2012

AMM

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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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Section: Information Technology Applications In Industrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method

Tang¹,

Chen²,

Cai³

2012

AMM

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“…The second filter is a High-Pass filter [8], which is shown in Fig.7. For this circuit, Vout is the only accessible node.…”

Section: Linear Circuitsmentioning

confidence: 99%

“…In our study, wavelet packet decomposition is employed to perform feature extraction and this technique has been addressed in [5] and [8]. For the linear circuits, the WPD technique is applied to the fault samples which are decomposed into approximations and details at level N (N=1, 2, 3…, segmented with dashed-lines shown in Fig.…”

Section: Feature Extractionmentioning

confidence: 99%

A Novel Approach of Analog Fault Classification Using a Support Vector Machines Classifier

Cui¹,

Wang²

2010

Metrology and Measurement Systems

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In order to make the analog fault classification more accurate, we present a method based on the Support Vector Machines Classifier (SVC) with wavelet packet decomposition (WPD) as a preprocessor. In this paper, the conventional one-against-rest SVC is resorted to perform a multi-class classification task because this classifier is simple in terms of training and testing. However, this SVC needs all decision functions to classify the query sample. In our study, this classifier is improved to make the fault classification task more fast and efficient. Also, in order to reduce the size of the feature samples, the wavelet packet analysis is employed. In our investigations, the wavelet analysis can be used as a tool of feature extractor or noise filter and this preprocessor can improve the fault classification resolution of the analog circuits. Moreover, our investigation illustrates that the SVC can be applicable to the domain of analog fault classification and this novel classifier can be viewed as an alternative for the back-propagation (BP) neural network classifier.

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