Markov reliability model research of monitoring process in digital main control room of nuclear power plant

Monitoring process is an important part in a high safety digital main control room of nuclear power plant (NPP), it is the source extracted information and found abnormal information in time. As the human factors events arisen from monitoring process recently take place more and more frequent, the authors propose a reliability Markov model to effectively decrease these abnormal events. The model mainly analyzes next monitoring object probability in terms of current information and plant state. The authors divide digital human–machine interface into two parts that are referred as logical homogeneous Markov and logical heterogeneous Markov. For the former, a series of methods of probability evaluation are proposed, such as, Markov transition probability with condition, probability distributed function with human factors, system state and alarm; for the latter, the authors propose the calculation of probability of correlation degree between last time and next time and probability calculation methods with multi-father nodes. The methods can effectively estimate the transition probability from a monitoring component to next monitoring component at time t, can effectively analyze which information is more important in next monitoring process and effectively find more useful information in time t + 1, so that the human factors events in monitoring process can greatly be decreased.

Research highlights
► This paper proposes a reliability Markov model that can analyze next monitoring object probability in terms of current information and state.
► The authors in the paper divide digital human–machine interface into two parts that are referred as logical homogeneous Markov and logical heterogeneous Markov.
► For homogeneous Markov model, the authors propose many dynamic functions.
► For heterogeneous Markov model, the authors consider the association degree of monitoring information.