A novel approach for fault detection of analog circuit by using improved EEMD

Fault detection and circuit testing consider as an important stage in production process. Fault detection is generally the last part of production process and after that the system is ready to use. Nowadays, controlling circuits and systems are widely used in control, communications, medical instruments and etc. the complexity of circuits are increased by growth of technology. Therefore, research and investigation on circuit fault detection is a challenging and non-ignorable issue. Many method has been presented in order to solve this problem. In this work methods based on signal processing and artificial intelligence will be used. Many of this methods such as wavelet transform, artificial neural networks, support vector machines, genetic algorithm and etc. were used in fault detection field. In this paper, a new method for the empirical mode decomposition of signals into independent IMF is presented, which results in extraction of discriminative features and better fault detection accuracy. The proposed method has been investigated and compared using two analog benchmark circuit. The results show a better performance of the proposed method.