Research on the health management of an automatic magazine control system to the naval gun

Abstract

According to the problems of low maintenance efficiency and high cost for traditional regular maintenance and after-the-fact maintenance methods, PHM technology is introduced to improve the success rate of the system operation, extend the service life, and reduce maintenance difficulty and cost. Aiming at the overall situation of the automatic magazine control system of a large-caliber gun weapon, a new health management system based on a data-driven method is proposed. Establishing a PHM model applied to automatic magazine control system by using the structure of open system architecture for condition-based Maintenance (OSA-CBM); the prediction and evaluation function of PHM system is studied with an example of transfer servo drive system; the prediction model based on BP neural network is established to predict the health parameters of the automatic magazine control system. Then the health state of system is evaluated according to the prediction result and the function is verified by simulation. The simulation results show this BP neural network model is applicable for the parameter prediction of the prediction and evaluation module of the automatic magazine PHM system. The prediction accuracy is over 90%, and the model has a good applicability.