ToolKit Markov Module

Markov Analysis is a powerful modeling and analysis technique with strong applications in the time-based reliability and availability analysis. The foundation of the analysis is based upon work done by the well known Russian mathematician Andrei Markov in the late 1800's. The reliability behavior of a system is represented using a state-transition diagram, which consists of a set of discrete states that the system can be in, and defines the speed at which transitions between those states take place. Markov models consist of comprehensive representations of possible chains of events, transitions, within systems, which correspond to sequences of failures and repair, or other states.

Markov state diagram

The Markov chain model is analyzed in order to determine the probability of being in a given state at a given point in time, the amount of time a system is expected to spend in a given state, as well as the expected number of transitions between states. States can be defined as "available" or "unavailable" states for the system.

Markov state models provide great flexibility in modeling the timing of events. They can be applied when simple parametric time-based models, such as Exponential or Weibull Time-to-Failure models are not sufficient to describe the dynamic aspects of a system's reliability or availability behavior, as may be the case for systems incorporating standby redundancy.

The Markov Module of ITEM ToolKit allows you to construct and analyze Markov models, as well as link them to Fault Tree, RBD, or Event Tree elements such as blocks and events.

Highlights:

Powerful graphical Markov chain and state model editor
Phased-mission models
Discrete- and continuous-time transition models
Flexible definition of states and groups of states
Features common to all modules

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