Analog Circuit Incipient Fault Diagnosis Method Using DBN Based Features Extraction

Correct identifying analog circuit incipient faults is useful to the circuit's health monitoring, and yet it is very hard. In this paper, an analog circuit incipient fault diagnosis method using deep belief network (DBN) based features extraction is presented. In the diagnosis scheme, time resp...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.23053-23064
Main Authors: Zhang, Chaolong, He, Yigang, Yuan, Lifeng, Xiang, Sheng
Format: Article
Language:English
Subjects:
Algorithms
Analog circuits
Bandpass filters
Belief networks
Circuit faults
Data mining
DBN
Electrical engineering
Fault detection
Fault diagnosis
Feature extraction
High pass filters
incipient fault diagnosis
Parameters
Particle swarm optimization
QPSO
Regularization
Support vector machines
SVM
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Summary:Correct identifying analog circuit incipient faults is useful to the circuit's health monitoring, and yet it is very hard. In this paper, an analog circuit incipient fault diagnosis method using deep belief network (DBN) based features extraction is presented. In the diagnosis scheme, time responses of analog circuits are measured, and then features are extracted by using the DBN method. Meanwhile, the learning rates of DBN are produced by using quantum-behaved particle swarm optimization (QPSO) algorithm, which is beneficial to optimizing the structure parameters of DBN. Afterward, a support vector machine (SVM) based incipient fault diagnosis model is constructed on basis of the extracted features to classify incipient faulty components, where the regularization parameter and width factor of SVM are yielded by using the QPSO algorithm. Sallen-Key bandpass filter and four-op-amp biquad high pass filter incipient fault diagnosis simulations are conducted to demonstrate the proposed diagnosis method, and comparisons verify that the proposed diagnosis method can produce higher diagnosis accuracy than other typical analog circuit fault diagnosis methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2823765