The intermittent fault diagnosis of analog circuits based on EEMD-DBN

Abstract

Aiming at the intermittent faults widely existing in analog circuits, caused by poor soldering and performance degradation of components, a method for the intermittent fault diagnosis of analog circuits based on ensemble empirical mode decomposition (EEMD) and deep belief network (DBN) is proposed. The amplitude and frequency anomaly signals in the frequency domain is regarded as triggering signals for intermittent fault detection, so the signal segments containing intermittent faults can be detected. The signal segment with intermittent fault is decomposed into multiple intrinsic mode functions (IMFs) by EEMD, and the IMF and fault features are optimized by using Pearson correlation coefficient and feature separability. The optimization feature set is established, which is used as feature vector to transmitted to DBN for fault diagnosis. The proposed method can autonomously realize the feature selection and the diagnosis of intermittent fault. The optimization features make DBN improve diagnostic accuracy, reduce diagnostic time, and can locate intermittent faults to the circuit branch with intermittent faults. The simulation experiments show that the proposed method has strong fault diagnosis capability. And the comparative experiments prove that compared with other commonly used methods, the EEMD-DBN method has higher diagnostic accuracy in intermittent fault diagnosis of analog circuits.