A susceptibility assessment method of high-power electromagnetic effects based on Gaussian process classification and autoregressive co-kriging model

High-power electromagnetic (HPEM) environments are capable of causing effects on systems, like malfunctions, performance degradation, interference and even collapse. To research susceptibility of systems under HPEM, experiment is believed as an efficient approach. But for some valuable and complex systems, we have no replacement nor technology to recover faulted system; otherwise, it will cost a heavy price. So we can't perform a lot of experiments and it's hard to collect enough effect data. How to assess susceptibility of systems under HPEM with limited effect data becomes an urgent problem for us. In this paper we introduce a novel statistical method, autoregressive co-kriging model, to deal with this problem. In this model, the real but small experimental data can be merged with rough but big data, such as related state experimental data and experience-based judgment data. Meanwhile, Gaussian process classification (GPC) is used to transform experimental binary data into continuous probability data to fit autoregressive co-kriging model. At the end of this paper, a case study of the susceptibility assessment of a supervisory control and data acquisition (SCADA) system under electromagnetic pulse is illustrated.