A novel approach for fault detection of analog circuit by using improved EEMD

Shokrolahi, Seyed Moslem; Kazempour, Alireza

doi:10.1007/s10470-018-1362-7

Cited by 25 publications

(8 citation statements)

References 18 publications

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“…In addition, Table 10 shows the accuracy for the second CUT and compares it with the studies by Shokrolahi and Kazempour 33 and Mahoaung et al 16 As shown, the proposed method with low-dimensional feature vectors (12 dimensions compared to 34 dimensions used in the study by Mahoaung et al 16 ) has yielded better results than the other two.…”

Section: Simulation Resultsmentioning

confidence: 86%

“…Nevertheless, the computational time of the algorithm for the first circuit (Table 11) and the second circuit (Table 12) has been significantly reduced. In Table 9, the accuracy rate of the proposed method at first CUT is compared with the methods in the studies by Shokrolahi and Kazempour, 33 Mahoaung et al, 16 and Zhang et al 20 In this case, it is observed that the proposed method with a low-dimensional feature vector outperformed the other three methods.…”

Section: Simulation Resultsmentioning

confidence: 95%

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Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

Moezi

Kargar

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposed a practical approach to isolating faults in analog circuits. The contribution of this article is twofold. First, the optimized empirical mode decomposition approach is presented based on the Hellinger distance such that there is a minimum dependency between intrinsic mode functions. Features with high distinction could be extracted by employing intrinsic mode functions in fault detection problem of analog benchmark circuits. Second, the non-dominated sorting genetic algorithm is employed to retain excellent features and speed up the execution, resulting in the high accuracy of fault detection and isolation. The number of features and mean squared error are selected as objective functions. The features from the data are also extracted using the fast Fourier and wavelet transforms for comparison. Finally, the support vector machine and artificial neural network are employed to isolate faults. Two circuits under test are simulated, and the output signals of the faulty and fault-free circuits are extracted by the Monte Carlo analysis. According to the obtained simulation results, the proposed method with a low-dimensional feature vector outperformed the previous methods, and the computational time has also reduced significantly.

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Section: Simulation Resultsmentioning

confidence: 86%

Section: Simulation Resultsmentioning

confidence: 95%

Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

Moezi

Kargar

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…After multiple decomposition, the injected white noise signals will cancel each other out. Finally, the average value of multiple decomposition of EEMD is used as the result in Shokrolahi and Kazempour [23]. The specific process is as follows:…”

Section: Eemd Decomposition Principlementioning

confidence: 99%

An Adaptive Detection Algorithm for Micro-grid Harmonic Power Based on Deep Belief Network

2021

Teh. vjesn.

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There are many non-linear load devices in the micro-grid, resulting in a lot of complex harmonics, which is a key problem that leads to low measurement accuracy of electric energy metering devices. The traditional integrated empirical mode decomposition (EEMD) method can effectively deal with the problem of nonlinear and nonstationary signals, but this method has the problem of being highly dependent on artificial pre-set parameters. Here, the deep belief network (DBN) is introduced in the white noise signal generation process of EEMD. The main problems solved are as follows: one is to adaptively match the white noise signal according to the data characteristics of the current signal in the micro-grid, the other is to reduce the artificial setting error and make the separation result closer to the theoretical value. Finally, this paper uses the operating data in the actual environment to carry out experimental verification, and the results show that the error between the value of harmonic power in the production environment and the theoretical value given is reduced by 9.73%.

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“…Viveros-Wacher and Rayas-Sánchez [ 20 ] investigated artificial neural networks for constraint parameter extraction and fault classification for analog fault identification in RF circuits. Shokrolahi [ 21 ] used Ensemble Empirical Mode Decomposition (EEMD) for preprocessing of analog circuit fault signals and extracted the fault features from the Intrinsic Mode Functions (IMF) components obtained based on EEMD for composing feature vectors. This method effectively improves the accuracy of analog circuit fault detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN

Yuan,

Sheng,

Zhuang

et al. 2024

PLoS ONE

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To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

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A novel approach for fault detection of analog circuit by using improved EEMD

Cited by 25 publications

References 18 publications

Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

An Adaptive Detection Algorithm for Micro-grid Harmonic Power Based on Deep Belief Network

A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN

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