Abstract: Largecaliber artillery can limit the enemy's movement to the maximum range at the least cost. It is a very critical fire suppression weapon on the battlefield. However, due to its harsh working environment, largecaliber artillery performs very unstable in missions. Based on the research project of the health management system of largecaliber artillery, while monitoring and recording the working status of largecaliber artillery in real time, this paper proposes a design idea of failure prediction and analysis of largecaliber artillery based on deep learning by combining expert analysis and other health ma nagement methods. The unsupervised and efficient feature extraction capabilities of the deep belief network and the supervised data classification capabilities of the multilayer perceptron are adopted to establish a fault prediction deep learning model, in order to realize the prediction of the failure state of largecaliber artillery and provide technical support for the premaintenance of largecaliber artillery, thereby improving the reliability of largecaliber artillery.

Key words: large-caliber artillery, fault prediction, deep belief network, multilayer perception