A model-based approach for event definition in support of flight data monitoring

Abstract

Flight data monitoring (FDM) systems promise safety improvements in flight operations through the use of on-board data from regular flights. FDM systems can provide data pertaining to many types of accidents where human factors have been implicated because they track the manner in which the vehicles are operated. For helicopters, most implementations of such systems on helicopters rely on experts to determine pre-set limits on combinations of flight parameters. These limits are also known as "safety events". A common practical problem that arises in FDM systems is the need to have sufficient knowledge of a condition before events can be defined and used in a proactive manner. There has been recent interest in using alternative approaches to detecting faults and unsafe events in aviation and solve this inherent limitation of FDM. In this work, a model-based approach is investigated for potential improvements to FDM practices through an objective analysis of the conditions that pertain to an accident. First, a rollover during taxi is investigated using a lateral dynamic model. Various scenarios are passed to the model and analyzed to identify the combinations of conditions that lead to a rollover. The result is a map between the input space and the outcome generated by the model, which can be used by analysts to define boundaries of safe operation. The second model is based on helicopter performance theory and is used with autorotation data. Using estimates of parameters which are not present in the data, an improved detection of existing conditions was achieved. These results suggest that models which contain the appropriate physics may provide potential benefits in generating safety information and enhance the performance of typical helicopter flight data monitoring systems.