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

Effective fault diagnosis of critical components is essential to ensure the safe and reliable operation of the entire system. This article deals with the fault diagnosis of transmitter/receiver (T/R) module, which is a critical component in the phased array radar system, by proposing a novel deep belief network (DBN) learning method. A sparse DBN based on Gaussian function is first constructed to automatically learn the relationship between monitoring data and component health conditions. With the trained sparse DBN, 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 (CGO) 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 DBN-based modeling methods, but also exceeds other intelligent methods.

Automated summary

This article proposes a novel deep belief network (DBN) learning method for fault diagnosis of transmitter/receiver (T/R) module. By constructing a sparse DBN based on Gaussian function to learn the relationship between monitoring data and component health conditions, pseudo-labels are generated for unlabeled samples and information entropy is used to reduce pseudo-label noise. The proposed method achieves a mean identification rate of 96.33%, surpassing some DBN-based modeling methods and other intelligent methods.