Clustering Function-Based Failure Analysis Results to Evaluate and Reduce System-Level Risks

For complex, safety-critical systems failures due to component faults and system interactions can be catastrophic. One aspect of ensuring a safe system design is the analysis of the impact and risk of potential faults early in the system design process. This early design-stage analysis can be accomplished through function-based reasoning on a qualitative behavior simulation of the system. Reasoning on the functional effect of failures provides designers with the information needed to understand the potential impact of faults. This paper proposes three different methods for evaluating and grouping the results of a function failure analysis and their use in design decision-making. Specifically, a method of clustering failure analysis results based on consequence is presented to identify groups of critical failures. A method of clustering using Latent Class Analysis provides characterization of high-level, emergent system failure behavior. Finally, a method of identifying functional similarity provides lists of similar and identical functional effects to a system state of interest. These three methods are applied to the function-based failure analysis results of 677 single and multiple fault scenarios in an electrical power system. The risk-based clustering found three distinct levels of scenario functional impact. The Latent Class Analysis identified five separate failure modes of the system. Finally, the similarity grouping identified different groups of scenarios with identical and similar functional impact to specific scenarios of interest. The overall goal of this work is to provide a framework for making design decisions that decrease system risks.