Research on comprehensive fault prediction model of tank fire control system based on machine learning

Due to the insufficient fault information of the tank fire control system and the complex fault characteristics, and the fault signal has the characteristics of high dimension, small sample and nonlinearity, the fault prediction of the fire control system is difficult and the reliability is low. In order to solve such problems, two intelligent predictive models for fire control systems for machine learning algorithms are proposed: multi-step prediction model of fire control system performance trend based on particle swarm improved support vector regression machine, and the fault state prediction model based on support vector classifier , constructs a failure decision function and performs intelligent prediction combined with lateral prediction and longitudinal prediction to improve the reliability of fault prediction. The two models were verified by the power module of the fire control computer and sensor subsystem in a certain type of tank fire control system. The experimental results show that the proposed fire control system fault prediction model has high accuracy and practicability.