EAGER: A Systems Approach to Predicting and Preventing Accidents During Operations

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This EArly-concept Grant for Exploratory Research (EAGER) project will contribute to the economic prosperity, safety and wellbeing of the nation by taking the first proof-of-concept steps in investigating a new approach to identifying leading indicators signaling the potential for an accident before it occurs. Despite our best intentions to develop safe products and services, accidents and losses still occur. As the potential for greater losses involved in our advanced technology today increases, it will be harder to design inherently safe systems and operational risk management practices will become more important. There are always warning signs before a major accident, but these signs may only be noticeable and interpretable in hindsight. At the same time, enormous amounts of data can now be collected by computers but most of theses data are not used because of the size and difficulty in creating useful information from the data. This research will take a new and unique approach to analyzing operational data and identifying when risk is increasing. Three international airlines are participating in the research by providing real operational data and their expertise to validate the feasibility and efficacy of the new risk analysis technique. While airline operations are used as the testbed, the research results can be extended to any safety-critical industry. The basic hypothesis to be evaluated in this research is that useful leading indicators can be identified based on the assumptions underlying the safety engineering practices employed and on the vulnerability of those assumptions. This process will require the use of various types of operational data that can be (and often is) collected but needs some process for determining the potential consequences of the collected data. The research will build on a new model of accident causation called STAMP (System-Theoretic Accident Model and Processes) and a new hazard analysis technique (STPA or System-Theoretic Process Analysis) to work on that model. STAMP extends current accident causality to include more complex causes than simply component failures and chains of failure events or deviations from operational expectations (which is the basis for traditional hazard analysis methods). STAMP incorporates basic principles of systems thinking and is based on systems theory rather than traditional reliability theory. A new analysis tool called Active STPA will be created that will use data commonly collected during operations to identify when the assumptions made during design and development of a product or service are violated during operations and thus may be a precursor for an accident and evidence that risk may be increasing. The test bed will be airline operations. While the subject of this research is limited to identifying leading indicators related to safety and accidents, the ideas will apply to leading indicators and risk management for system properties other than safety and could open a new path to evaluating risk during operations for researchers to exploreThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.