Development of a Metric Concept that Differentiates Between Normal and Abnormal Operational Aviation Data.

There is a strong and growing interest in using the large amount of high-quality operational data available within an airline. One reason for this is the push by regulators to use data to demonstrate safety performance by monitoring the outputs of Safety Performance Indicators relative to targeted goals. However, the current exceedance-based approaches alone do not provide sufficient operational risk information to support managers and operators making proximate real-time data-driven decisions. The purpose of this study was to develop and test a set of metrics which can complement the current exceedance-based methods. The approach was to develop two construct variables that were designed with the aim to: (1) create an aggregate construct variable that can differentiate between normal and abnormal landings (row_mean); and (2) determine if temporal sequence patterns can be detected within the data set that can differentiate between the two landing groups (row_sequence). To assess the differentiation ability of the aggregate constructs, a set of both statistical and visual tests were run in order to detect quantitative and qualitative differences between the data series representing two landing groups prior to touchdown. The result, verified with a time series k-means cluster analysis, show that the composite constructs seem to differentiate normal and abnormal landings by capturing time-varying importance of individual variables in the final 300 seconds before touchdown. Together the approaches discussed in this article present an interesting and complementary way forward that should be further pursued.

When Outcomes are not Enough: An Examination of Abductive and Deductive Logical Approaches to Risk Analysis in Aviation.

While airlines generate massive amounts of operational data every year, the ability to use the collected material to improve safety has begun to plateau. With the increasing demand for air travel, the aviation industry is continually growing while simultaneously being required to ensure the level of safety within the system remains constant. The purpose of this article is to explore whether the traditional analysis methods that have historically made aviation ultra-safe have reached their theoretical limits or merely practical ones. This analysis argues that the underlying logic governing the traditional (and current) approaches to assess safety and risk within aviation (and other safety critical systems) is abductive and therefore focused on creating explanations rather than predictions. While the current "fly-fix-fly" approach has, and will continue to be, instrumental in improving what (clearly) fails, alternative methods are needed to determine if a specific operation is more or less risky than others. As the system grows, so too does the number of ways it can fail, creating the possibility that more novel accidents may occur. The article concludes by proposing an alternative approach that explicitly adds temporality to the concepts of safety and risk. With this addition, a deductive analysis approach can be adopted which, while low in explanatory power, can be used to create predictions that are not bound to analyzing only outcomes that have occurred in the past but instead focuses on determining the deviation magnitude between the operation under analysis and historically commensurate operations.