An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits

Arabi, Abderrazak; Bourouba, Nacerdine; Belaout, Abdesslam; Ayad, Mouloud

doi:10.1016/j.vlsi.2018.08.001

Cited by 31 publications

(14 citation statements)

References 11 publications

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“…Meanwhile, these filters can be simply applied to verify the performance of the proposed method. On the other hand, the leapfrog filter is a more complex filter circuit with more critical components, which is widely applied for the fault diagnosis [22]. Therefore, the leapfrog filter is employed in the present study to demonstrate the wide applicability of the proposed 1D-CNN model.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…The circuit model and parameter settings, including the noise added to the dataset of circuit-3, are the same with those of section 3.1. In order to evaluate the performance of the proposed method, results obtained from the 1D-CNN classifier are compared with those of other classification methods, including ANFIS [22], DBN [17] and UFK-WNN [20]. All of these methods are based on the neural network algorithm.…”

Section: B Classification Experiments To Perform Fault Diagnosismentioning

confidence: 99%

“…Since establishment of modeling approaches is a great challenge, data-driven approaches have become more popular as they combine feature extraction and classification techniques into a single learning body. Reviewing the literature indicates that commonly used data-driven approaches are the artificial neural network (ANN) [17]- [22], support vector machine (SVM) [23], [24], and other machine learning methods [27], [28]. Zhao et al [17] proposed an intelligent solution based on the deep belief network (DBN), which has higher classification accuracy and lower requirements on data.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, a multiclass adaptive neuro fuzzy interface system (ANFIS) is proposed in Ref. [22], which incorporates both ANN and fuzzy logic systems. Tan et al [29] presented a systematic method, which combines neural networks and the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

Yang¹,

Meng²,

Wang³

2020

IEEE Access

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The present study applies the one-dimensional convolutional neural network (1D-CNN) to propose an intelligent approach of the feature extraction for the analog circuit diagnosis. The raw signals based on various soft faults from the output terminal of the circuit under test (CUT) are collected with appropriate data acquisition system to implement a data-driven fault diagnosis. The data-driven diagnosis process is typically encapsulated in two distinct blocks, including the feature extraction and the classification. In this study, the designed 1D-CNN model efficiently combines the aforementioned two phases into a single diagnosis body with fast learning rate and accurate classification. The main advantages of the 1D-CNN are: 1) it can be directly established to the raw signal with proper training so that it is more applicable in real applications; 2) its compact architecture and configuration has reasonable applicability in complex analog circuits; 3) convolutional kernels guarantee that the hierarchical features can be extracted from raw data with better anti-interference performance. Moreover, since the method can extract high-level features of raw signals, it resolves the necessity to employ other per-processing methods for the hand-crafted feature transformation. The performance of the proposed 1D-CNN model is evaluated through three benchmark circuits on the SIMULINK platform. Obtained results are compared with other intelligent fault diagnosis methods. The experimental results show that the 1D-CNN can be utilized effectively as the feature exactor and faults classifier for analog circuits. INDEX TERMS Analog circuit fault diagnosis, convolutional neural network, data-driven method, faults classification, feature extraction.

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Section: Simulation Experimentsmentioning

confidence: 99%

Section: B Classification Experiments To Perform Fault Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

Yang¹,

Meng²,

Wang³

2020

IEEE Access

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“…A variety of feature extraction algorithms have been developed involving time-domain analysis methods [3], frequency-domain analysis methods [4], time-frequency-domain analysis methods [5][6], information entropy method [7][8]. Besides, some shallow machine learning models have been applied for fault classification, such as neural network (NN) [9][10][11][12], support vector machine (SVM) [13][14][15][16][17], extreme learning machine [18], adaptive neurofuzzy interface system (ANFIS) [19]. Nevertheless, these methods rely heavily on engineering experience and much prior knowledge about failure symptoms and have poor generalization ability.…”

Section: Introductionmentioning

confidence: 99%

Incipient fault diagnosis of analog circuits based on wavelet transform and improved deep convolutional neural network

Yang

Wang

Nie

et al. 2021

IEICE Electron. Express

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To enhance the reliability of analog circuits in electrical systems, this letter proposes a novel incipient fault diagnosis method by integrating wavelet transform(WT) and improved convolutional neural network. Different from traditional methods, where feature extraction and classification are separately designed and performed, this letter aims to automatically learn fault features and classify the type of faults simultaneously. An improved convolutional neural network named multi-channel compactness convolutional neural network (MC-CNN) is proposed,which can obtain complementary and rich diagnosis information from multi-scale components extracted by wavelet transform. Moreover, we adopt center loss as an auxiliary loss function to maximize the interclass separability and intraclass compactness of samples. The proposed method is fully evaluated with the Sallen-Key bandpass filter circuit and the fouropamp biquad high-pass filter circuit. The experimental results demonstrate that the proposed method is very effective in feature extraction for fault diagnosis, and has higher diagnosis accuracy than other typical fault diagnosis methods.

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A Novel Fault Diagnosis Method for Analog Circuits Based on Conditional Variational Neural Networks

Gao

Yang

Jiang

et al. 2020

Circuits Syst Signal Process

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An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits

Cited by 31 publications

References 11 publications

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

Incipient fault diagnosis of analog circuits based on wavelet transform and improved deep convolutional neural network

A Novel Fault Diagnosis Method for Analog Circuits Based on Conditional Variational Neural Networks

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