Parameter Estimation for Quantitative Dependability Analysis of Safety-Critical and Control Systems of NPP

Abstract

Unified Modeling Language (UML) has been frequently used as one of the most powerful object-oriented graphical modeling tools for designing and modeling a safety-critical and control system since past decades. UML can capture all the system requirements, and the developed model is well understood by all the stakeholders. However, UML model has some limitations in demonstrating the dynamic behavior of a system. Furthermore, UML cannot be directly mapped to a mathematical modeling tool for a critical analysis. A state-space modeling technique overcomes the above-stated limitations of UML maturely and is useful for prognostics of safety-critical systems. However, existing methods to perform safety analysis using a state-space model are based on assumed state transition probabilities. The inaccuracies in predicting transition probability results translate into erroneous safety analysis—the extent of that error depends on a criticality of the system’s state. Thus, to have practical value, a prediction must compute probabilities, rather than assuming or qualitatively assess them. In this paper, a safety analysis framework is introduced, which uses a technique to map the UML model into the state-space model for capturing all the system requirements along with dynamic behavioral analysis and compute state transition probabilities, instead of taking assumed values, to analyze a safety-critical and control system. The validation of this approach is done on 29 different safety-critical and control systems of Nuclear Power Plant, and shown on the digital feed water control system, which consists of diversified network.