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Aminian, Farzan; Aminian, Mehran

doi:10.1023/a:1011141724916

Cited by 62 publications

(5 citation statements)

References 8 publications

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“…It has been proven in practice that many polynomials can satisfy this condition, although the polynomial proposed by Daubechies used by Gilles is widely used, as shown in (4).…”

Section: Theory Of Ewtmentioning

confidence: 99%

“…At the end of the 20th century, with the rise of artificial intelligence algorithms, a large number of fault diagnosis methods for analog circuits based on artificial intelligence algorithms were proposed, thereby realising the intelligence and automation of analog circuit fault diagnosis. From 2000 to 2007, Aminian used wavelet transform (WT) to preprocess the impulse response of the CUT and then realised the fault diagnosis of analog circuits by neural networks [3][4][5]. In 2008, Tan extracted features from the feasible domain based on wavelet packet transform (WPT) and finally completed the fault diagnosis of analog circuits by probabilistic neural networks [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

Liu

Xie

et al. 2022

Applied Sciences

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Analog circuits play an essential role in electronic systems. To strengthen the reliability of sophisticated electronic circuits, this paper proposes a novel analog circuit fault diagnosis method. Compared with traditional fault diagnosis, the fault diagnosis process in this paper uses a square wave as the stimulus of the circuit under test (CUT), which is beneficial for obtaining the response of the CUT with rich time and frequency domain information. The improved empirical wavelet transform (EWT), which can more accurately extract the amplitude modulated–frequency modulated (AM-FM) components, is used to preprocess the original response. Finally, based on the preprocessed data, a multi-input deep residual network (ResNet) is constructed for fault feature extraction and fault classification. The multi-input ResNet is a powerful approach for learning the fault characteristics of the CUT under different faults by learning the characteristics of the AM-FM components. The effectiveness of the method proposed in this paper is verified by comparing different fault diagnosis methods.

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“…It has been proven in practice that many polynomials can satisfy this condition, although the polynomial proposed by Daubechies used by Gilles is widely used, as shown in (4).…”

Section: Theory Of Ewtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

Liu

Xie

et al. 2022

Applied Sciences

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“…Among them, the feature extraction stage is a key part in the fault diagnosis process, which has an important influence on the fault diagnosis results. At present, there are many feature extraction methods applied to the fault feature extraction of analog circuits, such as the wavelet transform [3], decision tree [4], Bayesian network [5], support vector machine [6,7], artificial neural network [8], etc. However, the diagnostic model provided by the above method has poor nonlinear fitting ability.…”

Section: Introductionmentioning

confidence: 99%

Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network

2021

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Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.

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“…The problem is difficult because in modern fabrication process only a limited number of nodes is accessible for measurement and excitation, the values of faultfree elements are scattered within their tolerance ranges and some circuit elements may form ambiguity groups. Much works in this area exploit heuristic methods, artificial neural networks, evolutionary techniques, support vector machines, and elements of fuzzy logic [1,2,6,8,12,15]. Some researches concentrate on self-testing of analog circuitry of mixed-signal systems using built-in self test blocks, e.g., [5].…”

Section: Introductionmentioning

confidence: 99%

“…By comparing some quantities, obtained on the basis of measurement, with the patterns contained in the dictionary the fault can be located and identified. During the last decades many tools have been used to build and exploit fault dictionary, e.g., sensitivity analysis [14], neural networks [1,2], and the Householder formula in matrix theory [22].…”

Section: Introductionmentioning

confidence: 99%

Spot Defect Diagnosis in Analog Nonlinear Circuits with Possible Multiple Operating Points

2015

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The paper is focused on local spot defect diagnosis in nonlinear analog integrated circuits. The defects are simulated by finite resistors, high in the case of open and low in the case of short. A diagnostic method that allows detecting, locating, and estimating the value of the defect is developed. The method employs the simulation before test approach leading to a fault dictionary and brings a procedure for locating the defect and estimating its value, on the basis of some quantities measured during the diagnostic test. Because the nonlinear circuit under test may have multiple operating points, even if the fault-free circuit has a unique solution, building the fault dictionary requires a special approach. It is based on some families of characteristics, expressing the resistances that simulate the defects in terms of several voltages, taking into account the deviations of the fault-free parameters within their tolerance ranges. To illustrate the proposed approach two numerical examples are given.

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Cited by 62 publications

References 8 publications

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network

Spot Defect Diagnosis in Analog Nonlinear Circuits with Possible Multiple Operating Points

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