Fault diagnosis is crucial for analog circuits. In this paper, a new fault diagnosis method based on genetic algorithm and support vector machine (GA-SVM) is proposed. We design fault mode and collect the fault datasets on the basis of a quad high pass filter circuit. Wavelet packet analysis is employed to extract fault samples information. Sampled data's dimension is further reduced by Principal Component Analysis(PCA). To improve the efficiency of SVM, we use GA to search optimized parameters for its kernel function. After being trained with sampled data, the optimized SVM can steadily classify circuit faults. Simulation results show that the new algorithm classifies circuit faults at an accuracy of 92.69%. Our approach provides a new direction for analog circuit fault diagnosis.
|Conference||2013 IEEE International Conference of IEEE Region 10, IEEE TENCON 2013|
|Period||22/10/2013 → 25/10/2013|
|Sponsor||IEEE Region 10 (Asia Pacific Region), IEEE Xi'an Section, National Natural Science Foundation of China, Northwestern Polytechnical University, Xi'an Jiaotong University|
|Series||IEEE Region 10 Annual International Conference, Proceedings/TENCON|
- Analog Circuits