Multifragmental Markovxe2x80x99s Models for Safety Assessment of NPP I&C System Considering Migration of Hidden Failures The information and control systems of Nuclear Power Plant and other safety critical systems are considered as a set of three independent hardware channels including online testing system. Nuclear Power Plant information and control systems design on programmable platforms is rigidly tied to the Vmodel of the life cycle. Safety and availability during its life cycle are assessed using Markov and multifragmental models. The multifragmental model MICS32 contains an absorbing state in case of hidden faults and allows evaluating risks of xe2x80x9chiddenxe2x80x9d unavailability. The MICS42 model simulates the xe2x80x9cmigrationxe2x80x9d of states with undetected failures into states with detected faults. These models describe the functioning of the system and the complete elimination of software faults. Results of multifragmental modeling are compared to evaluate proof test period taking into account requirements for SIL3 level and limiting values of hidden fault probabilities. Multifragment models are included in the assessing method of implementation safety requirements of ICS on programmable platforms. The information technology of decision support in assessing and managing the implementation of the requirements for ICS safety is also considered.

Reliability and Availability Assessment in Practice High reliability and availability is a requirement for most technical systems. Reliability and availability assurance methods based on probabilistic models is the topic addressed in this talk. Nonstatespace solution methods are often used to solve models based on reliability block diagrams, fault trees and reliability graphs. Relatively efficient algorithms are known to handle systems with hundreds of components and have been implemented in many software packages. Nevertheless, many practical problems cannot be handled by such algorithms. Bounding algorithms are then used in such cases as was done for a major subsystem of Boeing 787. Nonstatespace methods derive their efficiency from the independence assumption that is often violated in practice. State space methods based on Markov chains, stochastic Petri nets, semiMarkov and Markov regenerative processes can be used to model various kinds of dependencies among system components. However, the resulting state space explosion severely restricts the size of the problem that can be solved. Hierarchical and fixedpoint iterative methods provide a scalable alternative that combines the strengths of state space and nonstatespace methods and have been extensively used to solve reallife problems. We will take a journey through these model types via interesting realworld examples chosen from IBM, Cisco, Sun Microsystems, and Boeing. These methods and applications are fully described in a recently completed book: Reliability and Availability Engineering: Modeling, Analysis and Applications, Cambridge University Press, 2017.