High level fault modeling and fault propagation in analog circuits using NLARX automated model generation technique

Farooq, Muhammad; Xia, Likun; Hussin, Fawnizu Azmadi; Malik, Aamir Saeed

doi:10.1109/icias.2012.6306132

Cited by 10 publications

(2 citation statements)

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“…Therefore, realizing soft fault diagnosis at the component level is still challenging. Over the past few decades, numerous scholars have delved into this field, proposing various types of fault diagnostic approaches [3][4][5][6]. The field of artificial intelligence has experienced remarkable advancement in recent years, enabling the formulation and successful application of various deep learning models for fault diagnosis due to their excellent independent feature extraction capabilities and outstanding complex process generalization capabilities [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models

Jia,

Yang,

et al. 2023

Sensors

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As a pivotal integral component within electronic systems, analog circuits are of paramount importance for the timely detection and precise diagnosis of their faults. However, the objective reality of limited fault samples in operational devices with analog circuitry poses challenges to the direct applicability of existing diagnostic methods. This study proposes an innovative approach for fault diagnosis in analog circuits by integrating deep convolutional generative adversarial networks (DCGANs) with the Transformer architecture, addressing the problem of insufficient fault samples affecting diagnostic performance. Firstly, the employment of the continuous wavelet transform in combination with Morlet wavelet basis functions serves as a means to derive time–frequency images, enhancing fault feature recognition while converting time-domain signals into time–frequency representations. Furthermore, the augmentation of datasets utilizing deep convolutional GANs is employed to generate synthetic time–frequency signals from existing fault data. The Transformer-based fault diagnosis model was trained using a mixture of original signals and generated signals, and the model was subsequently tested. Through experiments involving single and multiple fault scenarios in three simulated circuits, a comparative analysis of the proposed approach was conducted with a number of established benchmark methods, and its effectiveness in various scenarios was evaluated. In addition, the ability of the proposed fault diagnosis technique was investigated in the presence of limited fault data samples. The outcome reveals that the proposed diagnostic method exhibits a consistently high overall accuracy of over 96% in diverse test scenarios. Moreover, it delivers satisfactory performance even when real sample sizes are as small as 150 instances in various fault categories.

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Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models

Jia,

Yang,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A high level fault modeling method proposed in Ref. [10] is used to model transistor level analog faults. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Phasor Analysis Based Fault Modeling and Fault Diagnosis Methods for Linear Analog Circuits

Gao

Yang

Tian

2014

AMR

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Soft fault diagnosis and tolerance are two challenging problems in linear analog circuit fault diagnosis. To solve these problems, a phasor analysis based fault modeling method and its theoretical proof are presented at first. Second, to form fault feature data base, the differential voltage phasor ratio (DVPR) is decomposed into real and imaginary parts. Optimal feature selection method and testability analysis method are used to determine the optimal fault feature data base. Statistical experiments prove that the proposed fault modeling method can improve the fault diagnosis robustness. Then, Multi-class support vector machine (SVM) classifiers are used for fault diagnosis. The effectiveness of the proposed approaches is verified by both simulated and experimental results.

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SFG Based Fault Simulation of Linear Analog Circuits Using Fault Classification and Sensitivity Analysis

Bhattacharya

Ragamai

Kumar

2017

Communications in Computer and Information Science

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High level fault modeling and fault propagation in analog circuits using NLARX automated model generation technique

Cited by 10 publications

References 10 publications

A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models

A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models

Phasor Analysis Based Fault Modeling and Fault Diagnosis Methods for Linear Analog Circuits

SFG Based Fault Simulation of Linear Analog Circuits Using Fault Classification and Sensitivity Analysis

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