A new switched current circuit fault diagnosis approach based on pseudorandom test and preprocess by using entropy and Haar wavelet transform

Long, Ying; Xiong, Yuejun; He, Yigang; Zhang, Zhen

doi:10.1007/s10470-017-0950-2

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…There are a great deal of extraction methods for analog circuit faults were developed including (a) no preprocessing at all [10], [15], (b) wavelet transform feature [16], [17], (c) statistical features (range, mean, standard deviation, kurtosis, and entropy) [18]- [20], and (d) frequency-domain features [21], [22]. Most researchers use one of preprocessing method, such as method proposed in paper [10] uses output voltage as features to train classifier directly, but the average accuracy is not very well.…”

Section: Introductionmentioning

confidence: 99%

“…Most researchers use one of preprocessing method, such as method proposed in paper [10] uses output voltage as features to train classifier directly, but the average accuracy is not very well. The work in [16] uses entropy and Haar wavelet transfrom to processes the time signal, and the method has improved the fault diagnosis accuracy. Reference [23] proposed a method based on a fault dictionary that only use entropy feature to diagnose faulty types, but the fault types can not be diagnosed correctly when the ambiguity groups of entropy are overlapped.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits. INDEX TERMS Nonlinear analog circuit fault diagnosis, WPT, RCCA-SVM.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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“…Ordinary feature extraction methods mainly include PCA, wavelet analysis, kernel analysis, etc. [10][11][12][13]. These methods have their limitations.…”

Section: Introductionmentioning

confidence: 99%

Analog circuit fault diagnosis via FOA-LSSVM

2020

TELKOMNIKA

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At present, the research on fault detection and diagnosis technology is very significant to improve the reliability of the equipment, which can greatly improve the safety and efficiency of the equipment. This paper proposes a new fault detection and diagnosis means based on the FOA-LSSVM algorithm. Experimental results demonstrate that the algorithm is effective for the detection and diagnosis of analog circuit faults. In addition, the model also demonstrate good generalization ability.

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“…Due to tolerance and nonlinearity of electronic components, the original signal overlaps in both the traditional time domain and frequency domain. The fault feature extraction based on signal processing is one of the hot topics, where Hilbert-Huang Transform (HHT) [1], wavelet [2][3][4], and wavelet packet transform [5] can obtain the time-frequency features for fault diagnosis in analog circuits. Rényi's entropy [6], conditional entropy [4,7] and cross-wavelet singular entropy [8] are used for fault feature extraction, since the entropy can be used to measure the uncertainty and variation of information.…”

Section: Introductionmentioning

confidence: 99%

“…The fault feature extraction based on signal processing is one of the hot topics, where Hilbert-Huang Transform (HHT) [1], wavelet [2][3][4], and wavelet packet transform [5] can obtain the time-frequency features for fault diagnosis in analog circuits. Rényi's entropy [6], conditional entropy [4,7] and cross-wavelet singular entropy [8] are used for fault feature extraction, since the entropy can be used to measure the uncertainty and variation of information. In order to reflect the faulty information from different perspectives, the statistical properties of the fractional transform signals are proposed as the fault features [9], for example, distance, mean, standard deviation, skewness, kurtosis, entropy, median, third central moment, and centroid.…”

Section: Introductionmentioning

confidence: 99%

Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

et al. 2019

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The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.

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A new switched current circuit fault diagnosis approach based on pseudorandom test and preprocess by using entropy and Haar wavelet transform

Cited by 11 publications

References 24 publications

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Analog circuit fault diagnosis via FOA-LSSVM

Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

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