Fault Diagnosis of Analog Circuits Based on KPCA and SVM
| Autor: | Yang Li, Hengtong Wang, Chen Delong, Yinjng Guo, Rui Zhang, Pengfei Huan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer Science::Machine Learning
Diagnostic methods Analogue electronics business.industry Computer science Pattern recognition 02 engineering and technology Fault (power engineering) 01 natural sciences Kernel principal component analysis 010309 optics Support vector machine Svm classifier Search engine ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). |
| DOI: | 10.1109/imcec46724.2019.8983868 |
| Popis: | Aimed at the nonlinear properties of analog circuits features , kernel Principal Component Analysis(KPCA) combined with Support Vector Machine(SVM) based diagnostic method for faults of analog circuit was proposed in this paper. Here, uses KPCA to reduce the irrelevant features. The KPCA method is used to extract the initial features. The SVM is then applied to the circuit after features extraction. It can be used as a new idea for Fault Diagnosis of Analog Circuits. To better verify the superiority of the proposed method, KPCA+SVM classification results were compared with the results of PCA+SVM. It is concluded that KPCA+SVM classifier achieved a better performance than PCA+SVM in the terms of the accuracy. |
| Databáze: | OpenAIRE |
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