A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features

Gao, Tianyu; Yang, Jingli; Jiang, Shouda

doi:10.1109/tim.2020.3024337

Cited by 54 publications

(41 citation statements)

References 38 publications

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“…Numerous works in this field are focused on the problem of single fault diagnosis [5]- [7], because this is the most frequent case [8]. The diagnostic methods and techniques are based on circuit theory and signal processing methods [9]- [11], various branches of mathematics [7], [12]- [15], and Artificial Intelligence (AI) concepts [16]- [22].…”

Section: Introductionmentioning

confidence: 99%

“…Faults that occur in analog circuits can be divided into parametric and catastrophic. Different approaches have been proposed for parametric fault diagnosis [9], [16], [17], [23], [24]. Short and open in circuit connectivity are classified as catastrophic faults.…”

Section: Introductionmentioning

confidence: 99%

“…Methods developed using AI-based techniques have dominated the fault diagnosis of analog circuits in recent decades. These methods use different computation and optimization techniques such as Artificial Neural Networks (ANN) [2]- [4], [19], Support Vector Machine (SVM) [16]- [18], Fuzzy Logic Method (FLM) [4], Evolutionary Computation (EC) [19], Genetic Algorithm (GA) [20], and Particle Swarm Optimization (PSO) [4], [16]. Diagnostic methods based on AI allow a wide range of parameter tolerances and have high efficiency in assigning faults to the predefined classes.…”

Section: Introductionmentioning

confidence: 99%

“…The classes are learned and tested on this set. However, the process of creating the classifier required by many AI methods (e.g., [16]- [19]) requires different signal processing tools, statistical and heuristic methods and is time consuming. The process is carried out on a previously selected small set of tested elements with assumed values of the faulty parameter and the remaining parameters scattered randomly in their tolerance ranges.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Parametric and Catastrophic Fault Diagnosis of Analog Linear Circuits

Tadeusiewicz

Hałgas

2022

IEEE Access

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A universal method for single parametric and catastrophic fault diagnosis of analog linear circuits is presented in this paper. The methodology is based on fundamental laws governing linear circuits and methods of their analysis. The method involves creating models of the faulty elements, both passive and active, including current and voltage sources and applying an appropriately modified node method. This enables the creation of simple formulas to define the parameters of the faulty elements. Some measurement data must be collected during the course of the diagnostic test performed in the frequency domain and certain computation results obtained in the before-test-process. The method achieves all of the objectives of fault diagnosis: detection, location, and estimation of the faulty value. In 88.48%, the method correctly identifies the fault and estimates its value, but in 6.47%, the actual fault is accompanied by a virtual fault. The method is adapted to real conditions to improve the practical relevance and can be directly extended to double fault diagnosis. Six numerical examples are presented to illustrate the method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method for Parametric and Catastrophic Fault Diagnosis of Analog Linear Circuits

Tadeusiewicz

Hałgas

2022

IEEE Access

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“…With the wide application of electronic systems in aerospace, aircraft, robot, and other fields, improving the stability, security, and maintainability of electronic systems has become a fundamental issue in the circuit field [1]. Analog circuits are an essential part of electronic systems, although only 20% of electronic systems are analog circuits, 80% of electronic system failures are caused by them [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

et al. 2021

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This paper develops a novel soft fault diagnosis approach for analog circuits. The proposed method employs the backward difference strategy to process the data, and a novel variant of convolutional neural network, i.e., convolutional neural network with global average pooling (CNN-GAP) is taken for feature extraction and fault classification. Specifically, the measured raw domain response signals are firstly processed by the backward difference strategy and the first-order and the second-order backward difference sequences are generated, which contain the signal variation and the rate of variation characteristics. Then, based on the one-dimensional convolutional neural network, the CNN-GAP is developed by introducing the global average pooling technical. Since global average pooling calculates each input vector’s mean value, the designed CNN-GAP could deal with different lengths of input signals and be applied to diagnose different circuits. Additionally, the first-order and the second-order backward difference sequences along with the raw domain response signals are directly fed into the CNN-GAP, in which the convolutional layers automatically extract and fuse multi-scale features. Finally, fault classification is performed by the fully connected layer of the CNN-GAP. The effectiveness of our proposal is verified by two benchmark circuits under symmetric and asymmetric fault conditions. Experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

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Analog circuit fault diagnosis based on enhanced Harris Hawks optimization algorithm with RBF neutral network

Hao

et al. 2023

Engineering Reports

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Circuit faults are caused by the change of device parameters in the analog circuit. Aiming at the problems that the fault feature extraction is difficult and the fault signal cannot be effectively classified, an enhanced Harris Hawks algorithm is proposed to optimize the parameter optimization process in the RBF neural network, so as to realize the fault identification and diagnosis of the analog circuit. Based on wavelet packet analysis, the output response of the analog circuit is decomposed, and the fault feature vector is extracted. Taking the power conversion circuit in the electronic interlocking system as the research object, 500 sets of data are collected, and the EHHO‐RBF algorithm is trained and tested to realize the fault diagnosis of different faults, and compared with other neural network fault algorithms, the experimental results show the accuracy of fault diagnosis of EHHO‐RBF method is about 96.5%, which verifies the effectiveness and feasibility of the algorithm.

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A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features

Cited by 54 publications

References 38 publications

A Method for Parametric and Catastrophic Fault Diagnosis of Analog Linear Circuits

A Method for Parametric and Catastrophic Fault Diagnosis of Analog Linear Circuits

A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

Analog circuit fault diagnosis based on enhanced Harris Hawks optimization algorithm with RBF neutral network

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