A Novel Prognostic Model of the Degradation Malfunction Combining a Dynamic Updated-ARIMA and Multivariate Isolation Forest: Application to Radar Transmitter

Abstract

In the prognosis of radar transmitter degradation malfunction, there are some restrictions, such as the fact that it is difficult to obtain fault samples and the monitoring data cannot reach the fault threshold. For these restrictions, a novel data-driven prognostic method is proposed to predict the radar transmitter degradation malfunction, in which the dynamic updated-auto-regressive integrated moving average is proposed to be used to predict the subsequent time-step of the microwave measurement historical data, and the multivariate isolation forest established without fault samples is used to detect the degradation malfunction. The validity and portability of the model are verified using two-type of degradation malfunction prognostic experiments. The experimental results show that the degradation malfunction can be predicted at least 10 time-steps (100 min) before the occurrence of a degradation malfunction. Compared with the existing radar degradation malfunction prediction methods, the proposed method needs less historical data, no fault samples, no artificial thresholds, and no extracting features. This method can complete a degradation malfunction prognosis when there are relevant restrictions.