Collaborative Research: Improving the Safety of Complex Engineered Systems by Identifying Failure Paths Early in the Design Process

University Of Arkansas, Fayetteville AR

Investigators

Abstract

Complex engineered systems, such as those found in energy production, aviation and robotic industries rely on the integration of multiple technical, scientific and engineering disciplines. The traditional practice to ensure the safe operation of these systems is to use highly reliable components and to rigorously test potential design alternatives. Under this process, safety characteristics are often validated just prior to manufacturing instead of having a meaningful role in directing design from the earliest stages. This time and cost intensive approach of testing and redesigning after failures limits the competitiveness of the United States' advanced manufacturing capabilities. This award supports fundamental research for developing a new paradigm for safety-driven early-phase design, and for validating the corresponding system design theories. The immediate outcome of this research is a set of metrics that can allow designers to identify and compare the safety of different system alternatives early in the design cycle. Systems engineers have traditionally addressed safety only using heuristic reliability metrics or, towards the end of the design phase, using Monte Carlo analysis or Probabilistic Risk Analysis. Recent research in design decision making and formal systems modeling supports moving system analysis and testing forward in the design phase. Building on these recent trends, this award focuses on considering safety in early design. Specifically, this research aims to develop an understanding of system behaviors that lead to safe operation, by applying statistical clustering methods to thousands of complex failure scenarios to classify and understand system faults. The exploration of potential failure behaviors will be enabled through the use of hazard ontologies and search algorithms that automatically generate potential failure paths. Metrics will be identified to help system engineers identify the risk of hazardous system behaviors. Further, these metrics will enable designers to evaluate and compare design architectures and technological alternatives in the early design stage based on safety properties. The methods and safety-based assessments that are derived from this research will be validated using a novel complex system design testbed, consisting of a cooperative team of robotic rovers.