Analog Circuit Fault Diagnosis Based on Support Vector Machine

Abstract:

Analog circuit fault diagnosis is essentially a multiple state pattern classification problems. The traditional support vector machine classifier is for binary classification problems. In more than three kinds of commonly used class promotion model. This paper adopted the decision directed acyclic graph of multi-value classification algorithm. Multi-fault SVM classifier model is established. And the kernel function selection and nuclear parameter determination method were studied. Based on this model, support vector machine is used for analog circuit fault diagnosis given the basic idea and implementation steps. In analog circuit fault feature extraction technology, the effective sample point voltage amplitude response signal as well as the fault characteristic samples are extracted by wavelet packet decomposition of the energy spectrum method to extract the signal fault characteristics as the fault samples, formed based on effective sampling points. The SVM classifier is based on wavelet packet decomposition and the SVM classifier two methods of analog circuit fault diagnosis.

• Periodical: Advanced Materials Research (Volume 1055)
• Pages: 99-102
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1055.99
• Online since: November 2014
• Authors: Jun Shi, Tong Shu
• Keywords: Analog Circuit Fault Diagnosis, Binary Fractal Gridding Dimensions, Fault Features Extraction, Support Vector Machine (SVM)

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