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Fault Diagnosis of Analog Circuit Based on Multi-Input Convolution

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DOI: 10.23977/jeis.2022.070115 | Downloads: 34 | Views: 505

Author(s)

Bin Gong 1, Xianjun Du 1,2

Affiliation(s)

1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2 Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China

Corresponding Author

Bin Gong

ABSTRACT

Due to the low ability of fault feature extraction in analog circuits, it is impossible to classify components in analog circuits. A multi-input convolutional neural network (MIL-CNN) model based on attention mechanism is proposed. In the fault diagnosis experiment, the circuit of the two-stage four-op amplifier double-second order low-pass filter of the model has better comprehensive performance and can effectively realize the efficient classification and location of all faults.

KEYWORDS

Analog circuit, multiple-convolutional neural networks, Feature fusion, Fault diagnosis

CITE THIS PAPER

Bin Gong, Xianjun Du, Fault Diagnosis of Analog Circuit Based on Multi-Input Convolution. Journal of Electronics and Information Science (2022) Vol. 7: 89-93. DOI: http://dx.doi.org/10.23977/jeis.2022.070115.

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