期刊
IEEE ACCESS
卷 6, 期 -, 页码 23053-23064出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2823765
关键词
Analog circuits; incipient fault diagnosis; DBN; SVM; QPSO
资金
- National Natural Science Foundation of China [51607004, 51577046]
- State Key Program of National Natural Science Foundation of China [51637004]
- National Key Research and Development Plan Important Scientific Instruments and Equipment Development'' [2016YFF0102200]
- Equipment Research Project in Advance [41402040301]
- Anhui Provincial Natural Science Foundation [1608085QF157]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据