Pseudo-Label Guided Sparse Deep Belief Network Learning Method for Fault Diagnosis of Radar Critical Components

Abstract

Effective fault diagnosis of critical components is essential to ensure the safe and reliable operation of the entire system. This paper deals with the fault diagnosis of transmitter/receiver module, which is a critical component in the phased array radar system, by proposing a novel deep belief network learning method. A sparse deep belief network based on Gaussian function is first constructed to automatically learn the relationship between monitoring data and component health conditions. With the trained sparse deep belief network, the pseudo-labels are produced for unlabeled samples, while the information entropy is employed to calculate the confidence levels reflecting their certainty to reduce the effect of pseudo-label noise. The pseudo-labeled samples with high confidence levels are added to the training set to retrain the network. Optimal model configuration parameters are obtained through a chaos game optimization algorithm. The effectiveness of the proposed method is verified on a real-world dataset from a certain type of phased array radar. The experiments show that the mean identification rate of this method can reach 96.33%, which not only exceeds some deep belief network-based modeling methods, but also exceeds other intelligent methods.