Engineering risk and failure analysis focuses on predicting the probability of those (presumably rare) failures in an engineered system that can lead to severe damage to the system, injury, loss of life, and/or perhaps damage to the surrounding environment. Vulnerability analysis focuses on identifying (and reducing) the vulnerability of engineered systems to both natural (e.g., weather-related) and man-made (e.g., sabotage, terrorism) disruptions.
These analyses are typically used to inform decisions about required levels of redundancy and other design features, and to evaluate system safety and risk. In these kinds of studies, the output of the analysis is typically the probability of a particular high consequence outcome (e.g., catastrophic failure of the system), and identification of those events or components most likely to lead to that outcome. Based on this information, specific actions can be identified (e.g., design changes or modification of operating procedures) to reduce the risk.
By combining the flexibility of a general-purpose and highly-graphical probabilistic simulation framework with specialized features to support reliability analysis, GoldSim allows you to create quantitative and transparent risk, failure and vulnerability analysis models to allow you to ask "what if" questions regarding various designs and make defensible risk management decisions. GoldSim is flexible and powerful enough to allow you to create a “total system” model that represents the interactions, interdependencies and feedbacks between the various system components (including humans). Without such a model, it may not be possible to identify potential failure mechanisms, fatal flaws or system incompatibilities.
In particular, the GoldSim Reliability Module provides powerful features to support engineering risk and reliability analysis. The Reliability Module can be used to compute the probability of specific consequences (e.g., catastrophic failure of the system). GoldSim catalogs and analyzes failure scenarios, which allows for key sources of unreliability and risk to be identified.