A Degradation Fault Prognostic Method of Radar Transmitter Combining Multivariate Long Short-Term Memory Network and Multivariate Gaussian Distribution

In the prognosis of radar transmitter degradation fault, there are some problems, such as the total sample size and fault sample size of sensor monitoring data are small, and the monitoring data can not reach the fault threshold. To solve these problems, a prediction model combining the multivariate long short-term memory networks with multivariate Gaussian distribution is proposed, in which the long short-term memory networks predict the subsequent time step of multi-sensor monitoring data, and the multivariate Gaussian distribution model constructed by few fault samples is used to realize the prediction of degraded faults. The key parameters of the model are determined by the data processing experiments of double monitoring points, and the validity of the model is verified by the data processing experiments of multiple monitoring points. The experimental results show that the degradation fault can be predicted effectively within 10% of the total time series of monitoring data. Compared with the traditional radar warning after failure, the model can effectively predict the degradation fault of the transmitter when the fault sample size is low and the fault threshold is not reached.