Analog Circuits Soft Fault Diagnosis Using Rényi’s Entropy

Xie, Xuan; Bi, Dongjie; Zhou, Qizhong; Xie, Sanshan; Xie, Yongle

doi:10.1007/s10836-015-5520-x

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…Unfortunately, the membership function in Fen is usually difficult to determine. Some achievements in fault detection have been made using cross entropy and Rényi's entropy [24,25], but the faulty components have not been located. Moreover, none of these techniques are used to extract features with wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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Abstract:In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

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Section: Related Workmentioning

confidence: 99%

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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“…Heuristic computations and statistical analysis are used in [4,[6][7][8]13,18,20] for catastrophic and global parametric faults detection as well as for optimal test points and testing signal shape searching, respectively. In [34], the entropy parameter is used to analog circuit soft faults detections. The authors of work [12] present the concept of circuit under test (CUT) with faulty elements detections by means of estimated transfer function coefficients testing.…”

Section: Introductionmentioning

confidence: 99%

Analog Circuit Specification Testing by Means of Walsh–Hadamard Transform and Multiple Regression Supported by Evolutionary Computations

Golonek

Machniewski

2017

Circuits Syst Signal Process

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This paper describes an approach for an analog electronic circuit specification quick verification that is based on a simple mathematical model determined during the before-test stage statistical analysis. Generally, the observed performance parameters (e.g., cutoff frequency, phase shift at the assumed frequency point) are calculated from the Walsh-Hadamard transform components of circuit under test step time response. The optimal estimating formulas for relationship between the tested specifications and the selected components of the spectra are defined in the system based on a linear multiple regression procedure supported by genetic programming. The evolutionary computations significantly improve the approximation effectiveness by selecting the most representative points of the sequences domain and by defining the optimal set of linearizing functions. Finally, at the test stage, only a simple step stimulus and time-effective calculations are required to the tested performance parameters (specifications) of analog circuit identification and it predisposes the approach to quick production testing procedure realization or to embedding it in the mixed-signal systems. The automated built-in self-testing procedure using this concept may be easily implemented to low-cost microcontroller equipped with AD converter used for the testing response samples acquiring.

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“…As one of the foremost stages in fault diagnosis, feature extraction methods are closely related to the efficiency of fault diagnosis. Many feature extraction methods have been proposed such as correlation function technique [4], information entropy approach [5], the fast Fourier transform technique [6], and the wavelet transform technique [7]. Zhang et al [8] directly used the output voltage as features for fault diagnosis of analog circuits without preprocessing methods, and the results of fault diagnosis are not very good.…”

Section: Introductionmentioning

confidence: 99%

“…For information entropy techniques, it is more sensitive to parameter variations of components in CUTs. Therefore, information entropy is widely used with other techniques for fault diagnosis [5, 10–12]. Xie et al diagnosed soft faults of analog circuits using Rényi's entropy and the result is effective [5].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, information entropy is widely used with other techniques for fault diagnosis [5, 10–12]. Xie et al diagnosed soft faults of analog circuits using Rényi's entropy and the result is effective [5]. In [11], authors have developed a new fault diagnosis approach by using kurtosis and entropy of sampled signals as feature vectors to train a neural network classifier.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

Xiong

Tian

Yang

2016

Computational Intelligence and Neuroscience

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This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

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Analog Circuits Soft Fault Diagnosis Using Rényi’s Entropy

Cited by 18 publications

References 14 publications

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Analog Circuit Specification Testing by Means of Walsh–Hadamard Transform and Multiple Regression Supported by Evolutionary Computations

Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

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