Fault diagnosis method for fire control system based on empirical wavelet transform and relevance vector machine

The fire control system is an extremely important part of the tank and directly determines whether the tank can accurately hit the target. In extremely sophisticated fire control system devices, the signals generated by faults are mostly non-stationary, nonlinear, multi-component complex signals. In order to improve the accuracy of fault diagnosis of fire control systems, it is necessary to analyze and process complex signals more accurately. In this paper, a fault diagnosis method for fire control system is proposed. The acquired signal is denoised and extracted by empirical wavelet transform (EWT). The extracted signal is sent to the trained relevance vector machine (RVM) model. To achieve fault diagnosis of the fire control system.