Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Nasser, Ahmed R.; Azar, Ahmad Taher; Humaidi, Amjad J.; Al-Mhdawi, Ammar K.; Ibraheem, Ibraheem Kasim

doi:10.3390/electronics10232888

Cited by 22 publications

(16 citation statements)

References 27 publications

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“…Data-driven methods [9][10][11][12] require data obtained under faulty conditions to be available either through testing, operation, or simulation such that a comparison can be made to data obtained under healthy conditions for fault diagnosis. Features of the data are used for this comparison and can be time domain, frequency domain, or time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

2022

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Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.

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Section: Introductionmentioning

confidence: 99%

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

2022

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“…However, in the application of these two algorithms, this paper mainly focuses on the information expression of the internal structure of the first-order spatial mode. Further in-depth research is needed on the diagnosis of compressor stall inception, such as a statistical analysis with fuzzy logic classifier [29].…”

Section: Discussionmentioning

confidence: 99%

Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability

et al. 2022

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In order to effectively identify the signs of instability in the aerodynamic system of an axial compressor, a wavelet singular spectral entropy algorithm incorporated within the wavelet transform, singular value decomposition and information entropy is proposed to describe the distribution complexity of the spatial modalities in the flow field. This kind of identification design can accurately distinguish the boundary between the stable and unstable states of the internal flow field from the view of a dynamic system. On the basis of the information entropy algorithm, the wavelet singular spectral entropy algorithm is designed to integrate with the advantages of wavelet transform analysis on the time-frequency localization and singular value decomposition for signal processing and data mining together. So that the quantitative analysis of the definition of rebuilding a system image can be achieved by the solution of wavelet singular spectral entropy. This method can automatically extract the transient information of the space mode in the time-frequency domain. It effectively avoids the shortcoming that the feature extraction on spatial information cannot be accomplished from multiple angles with the single information entropy algorithm. In the data processing of instability signals under different speeds, the wavelet singular spectral entropy algorithm shows a greater advantage in the early warning for compressor stall. The result shows that the value of the wavelet singular spectral shows an obvious mutation when the aerodynamic system approaches the instability boundary. According to the threshold set, the identification hybrid algorithm can detect the stall precursor about 23~96 r in advance. Compared to the single information entropy algorithm, the hybrid wavelet singular spectral entropy algorithm is able to shift to an earlier precursor identification by about 11~82 r. This established hybrid identification algorithm accounts for the nonlinearity of the aerodynamic system, providing a new perspective for the nonlinear system instability identification.

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“…The selection of an effective automatic fault diagnosis for analog circuits has attracted much attention. 1 At present, there are many practical diagnostic methods, such as traditional fault dictionary method, 2 element component identification method and modern data intelligent processing technology, such as fuzzy theory, 3,4 wavelet transform, 5 support vector machine, 6 genetic algorithm, 7 and particle swarm algorithm. 8 However, the fault dictionary method can only diagnose circuit faults stored in the predetermined fault dictionary.…”

Section: Introductionmentioning

confidence: 99%

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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Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Cited by 22 publications

References 27 publications

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability

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

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