Circle Equation-Based Fault Modeling Method for Linear Analog Circuits

Tian, Shulin; Yang, Chen; Fang, Chen; Liu, Zhen

doi:10.1109/tim.2014.2307993

Cited by 20 publications

(25 citation statements)

References 60 publications

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“…Among SBT, data-driven diagnostic methods are based on the case that features of the system relatively changed when a fault happens. They extract features from output signals, then apply pattern recognition techniques such as neural networks (NNs) and support vector machines (SVMs) to locate a fault [8]. Meanwhile, the data-driven techniques do not need to construct an explicit model.…”

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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“…In addition, both the 3D model 1 3D (⋅) proposed in [14], and the complex-circle-based fault model proposed in [12,13] are adopted for comparison. In the example of 2 and 1 in the Sallen-Key filter, distances between 2 and 1-loci are provided in Table 4, according to each fault model, respectively; the comparison range is from ×10 −2 to ×10 2 , where is the fault-free value of 2 and 1.…”

Section: Simulation Example Of Sallen-key Filter For the 3d Modelmentioning

confidence: 99%

“…In the example of 2 and 1 in the Sallen-Key filter, distances between 2 and 1-loci are provided in Table 4, according to each fault model, respectively; the comparison range is from ×10 −2 to ×10 2 , where is the fault-free value of 2 and 1. The results of comparison between different methods are also illustrated in Figure 5, in which the curve 1 is the distance of 2-1 in the 3D model 2 3D (⋅), the curve 2 is the distance defined in the 2D model, the dash curve 3 is defined by 1 3D (⋅) [14], and dash curve 4 is related to complex-circle-based fault model [12,13]. As shown in Table 4, 3 is better than 4; however, because the biggest gap between 3 and 4 is less than 2.2%, 4 is very close to 3 in Figure 5.…”

Section: Simulation Example Of Sallen-key Filter For the 3d Modelmentioning

confidence: 99%

“…Curve 1 is defined by the 3D model proposed in Section 3, 2 is the 2D model proposed in Section 2, 3 is corresponding to the 3D model proposed in [14], and 4 is corresponding to the complex-circle-based fault model proposed in [12,13]. and the advantage of 1 is more significant.…”

Section: Simulation Example Of Sallen-key Filter For the 3d Modelmentioning

confidence: 99%

“…To achieve detection of parametric faults for analog circuits, Yang et al proposes a complex-circle based fault model [3]. Although this proposed fault model is improved sequentially by optimal testing frequency selection [12] and fault location [13], the weakness of fault masking still remains. Moreover, a 3D model based on transfer function was proposed in [14]; it extends the distances between fault loci and improves the practicability for applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

Tian²,

Guo³

2015

Journal of Applied Mathematics

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This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D) fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in threedimensional (3D) complex space is proposed, which achieves a far better fault detection ratio (FDR) against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT) method. By adding redundant bypassing-components in the circuit under test (CUT), this method achieves excellent fault isolation ratio (FIR) in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

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Fault Diagnosis of Analog Filter Circuit Based on Genetic Algorithm

Yang

Liu

2019

IEEE Access

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Hard (open and short) faults and discrete parameter faults (DPFs) are the mostly used fault models in the simulation-before-test (SBT) method. Because the parameter of the analog element is continuous, the DPF cannot elaborately characterize all possible continuous parameter faults (CPF) occurring in the analog circuit. To address such a problem, a genetic algorithm (GA)-based simulation after the test (SAT) fault diagnosis method is proposed in this paper. The fault diagnosis is transformed into an optimization problem. The genes represent the parameter values of potential faulty components. The faulty circuit response is the objective. Our target is to minimize the difference between the actual faulty response and the GA simulated response. The chromosome that minimizes the difference gives the solution. This method does not save all possible faults in advance while it can diagnosis all continuous fault values. The effectiveness of the proposed method is examined by using filter circuit examples.INDEX TERMS Fault diagnosis, genetic algorithm, optimization.

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Circle Equation-Based Fault Modeling Method for Linear Analog Circuits

Cited by 20 publications

References 60 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

Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

Fault Diagnosis of Analog Filter Circuit Based on Genetic Algorithm

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