Abstract
Standby redundancy is a widely-used technique to increase the reliability of many mission critical or life-critical systems, such as space missions, flight controls, nuclear power plants and monitoring systems. In the case of a failure occurring, the standby redundancy technique enables these systems to continue their intended function. Standby redundancy can introduce dynamics into the component failure rate and system failure behavior, posing difficulty in the system reliability modeling and analysis. While the existing analytical methods have various limitations (in, e.g. distribution type, computational complexity), Monte Carlo simulations (MCS) provide a powerful means to evaluate reliability of standby sparing systems. However, the existing works using this method focus only on specific use cases. This thesis proposes a reliability analysis framework based on the MCS method to address this issue. The framework models fault trees using Extensible Markup Language and MongoDB to store the elements of the model. The MCS algorithm and the framework are implemented using Scala and Java and tested using three case studies. The framework also provides the capability to evaluate failure importance indices for system elements to indicate their criticality to the functionality of the system. This work also implements a user interface built on top of the framework for ease of use.