Research on Risk Situation Awareness and Prediction Method of Industrial Control Systems

In the operation process of the power plant, in addition to the operating state information of the power plant process systems and components is monitored by Industrial Control Systems (ICSs), a large number of the operating state information of ICSs and its components themselves can be obtained. These information helps operators to understand the operation safety state of ICSs themselves, and whether ICSs operate reliably, which plays an important role in maintaining the normal operation of the power plant, controlling abnormal events and mitigating the development of accidents. This paper proposes an integrated methodological framework for real-time risk awareness and prediction of ICS based on the particle filtering model whose features are updating monitoring data and predicting the state transition process, and focus on the measurement of risk states, identification of critical risk states and key components, modeling of the state transition process, prediction of risk levels and estimation of remaining arrival time of high-risk residual. The assumption that the transition probability matrix of system states does not change over time in traditional Markov predictions is discarded in this proposed method. Compared with the traditional ICS safety analysis method, this method can provide global and systemic risk insights for ICS risk situation, with a better real-time performance, and it is a kind of more scientific and objective measurement for ICS risk situation.