Abstract

Future helicopter requirements, including expanded missions and single-pilot operation, will greatly increase the demands placed on the pilot. To meet these requirements without overwhelming the pilot, novel approaches to cockpit automation must be devloped. To assess the feasibility of applying Artificial Intelligence technology to helicopter cockpit automation, an expert system for status monitoring and diagnosis designated HELIX (HELicopter Integrated eXpert) has been developed.

At the heart of the HELIX program is a Qualitative Reasoning System (QRS). The QRS is a general mechanism to support the creation of hierarchical device models and reasoning about device behaviour using Qualitative Physics. The HELIX qualitative model is represented as a set of constraints that define the normal behaviour of the engines, transmission, flight controls, and rotors of the helicopter. Aircraft health is assessed by determining whether observations (sensor readings and pilot control inputs) are consistent with the constraints of the model. If an inconsistency is detected, a process of systematic constraint suspension is used to test various failure hypotheses.

Critical to the efficient operation of the HELIX program is the hierarchical model representation, which enables reasoning at various levels of abstraction. Using a top-down approach, the diagnostic process exploits the hierarchy by beginning fault isolation with the most reduced form of the model. To refine the diagnosis, a branch of the hierarchy may be expanded until a component-level diagnosis is made. The hierarchy also greatly reduces the complexity of multiple failure diagnosis. Rather than considering combinations of failures in all leaf components, the diagnosis can be restricted to combinations of branches in the hierarchy.

HELIX has been successfully tested on a variety of simulated failures. By representing only the normal behaviour of the helicopter and testing hypotheses by constraint suspension, HELIX has been able to diagnose single or multiple failures without prior knowledge of failure modes. The approach represents a promising technique for automating the qualitative reasoning required to diagnose novel failures and may form the basis for extensive automation both in airborne and ground-based diagnostic systems.

Author Keywords: diagnostics; expert systems; qualitative physics; helicopters

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