Model-Enhanced Analysis of Flight Data for Helicopter Flight Operations Quality Assurance

Helicopter Flight Operations Quality Assurance (HFOQA) systems promise safety improvements in flight operations through the use of on-board data from regular flights. HFOQA 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 HFOQA 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 to solve this inherent limitation of HFOQA. In this work, a model-based approach is taken in an effort to extend the capabilities of traditional HFOQA analysis, particularly in terms of definition and detection of monitored conditions. For localized conditions, the use of simple models in place of traditional safety events is investigated and demonstrated. The detection based on the model evaluation shows a good correspondence to the flight condition it was designed to capture, and at the same time incurring a minimal amount of additional computation. The model-based boundaries typically account for changes in vehicle parameters and operating conditions, whereas traditional safety events have to be modified through an iterative process. For a more general approach, a dynamic model was considered. By evaluating the vehicle's response to a set of control inputs spanning a range about the trim state, it was possible to determine the boundaries of safe input. The inputs falling outside this boundary were also evaluated for the risk associated with them based on the time required to reach a critical state. In both cases, the results offer improvements over the current state-of-practice, and can be deployed in a data-monitoring system directly or following intermediate post-processing steps.