GRIF I Markovian package - Assessing risk using Markov graphs
About the Markov module
This module is designed to create multi-phase Markov chains in order to assess the reliability, availability and safety of system architecture.
The Markov module is used to model systems in the form of Markov graphs. It produces simple models suited to all sectors (aeronautics, automotive, rail, oil & gas, etc.) and provides a large amount of data, including the availability and Lambda Equivalent of a system over time. The Markov module is based on efficient matrix computation algorithms, and uses ALBIZIA, the Markovian and BDD calculation engine developed by TotalEnergies.
Benefits of the Markov module
User-friendly
and ergonomic
Efficient, accurate
calculations (ALBIZIA)
Multi-phase
Markov chains
Modeling across all sectors of activity
GRIF is designed for any field of activity
Whether for an oil platform, aircraft, train or water supply system, GRIF evaluates the reliability and availability of any system using a range of computational techniques. The GRIF software suite offers a wide variety of calculation methods so that users can select the most appropriate (analytical, simulation, etc.), according to the system being modeled.
Minimum System Requirements
- Hardware requirement: Intel Core i3 or faster, 4 GB of free RAM, 1 GB of free space, no internet connection needed;
- Software requirements: Windows 10/11 or Linux or MacOS X with Java 11;
- Licences: standalone with USB dongle or floating licences with Sentinel server;
- Trial version available on our website.
Need more information?
About GRIF
Identify the essential indicators of your system analysis.
GRIF (GRaphical Interface for Reliability Forecasting) is a software suite designed to help engineers determine the key RAMS (Reliability - Availability - Maintainability - Safety) indicators. Drawing on TotalEnergies' experience in simulation research and development, it features mature, high-performance calculation engines and modeling capabilities to meet all your needs in any reliability study.