A novel fault diagnosis model research for electronic circuit

Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. A new optimization method for the fault diagnosis model is proposed. To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, an integrated fault diagnosis model based on support vector regression and principal components analysis is proposed in the paper. Utilizing the character that principal components analysis algorithm, the reduces of the original dataset are calculated and used to train individual SVR classifier for ensemble, which increase the diversity between individual classifiers, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The real electronic circuit data sets are used to investigate its feasibility in fault diagnosis. The results show that the proposed method is a promised ensemble method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.