Markov Chain Analysis

“Markov analysis (MA) is an analysis technique for modeling system state transitions and calculating the probability of reaching various system states from the model. MA is a tool for modeling complex system designs involving timing, sequencing, repair, redundancy, and fault tolerance. MA is accomplished by drawing system state transition diagrams and examining these diagrams for understanding how certain undesired states are reached and their relative probability. MA can be used to model system performance, dependability, availability, reliability, and safety. MA describes failed states and degraded states of operation where the system is either partially failed or in a degraded mode where some functions are performed while others are not.

 Markov chain: sequence of random variables in which the future variables is determined by the present variable but is independent of the way in which the present state arose from its predecessors (the future is independent of the past given the present). The Markov chain assumes discrete states and a discrete time parameter, such as a global clock. “

Reference: Chapter 18, Hazard analysis techniques for system safety. Clifton A. Ericson II

Technical Tools/Risk Assessment/Quantitative Risk Assessment/Frequency Techniques