A Model-based Health Monitoring and Diagnostic System for the UH-60 Helicopter
Ann Patterson-Hine
William Hindson
Dwight Sanderfer
NASA Ames Research Center
Somnath Deb
Chuck Domagala
Qualtech Systems, Inc.
Abstract
Model-based reasoning techniques hold much promise in providing comprehensive
monitoring and diagnostics capabilities for complex systems. We are exploring the use
of one of these techniques, which utilizes multi-signal modeling and the TEAMS-RT
real-time diagnostic engine, on the UH-60 Rotorcraft Aircrew Systems Concepts
Airborne Laboratory (RASCAL) flight research aircraft. We focus on the engine and
transmission systems, and acquire sensor data across the 1553 bus as well as by direct
analog-to-digital conversion from sensors to the QHuMS (Qualtech health and usage
monitoring system) computer. The QHuMS computer uses commercially available
components and is rack-mounted in the RASCAL facility. A multi-signal model of the
transmission and engine subsystems enables studies of system testability and analysis of
the degree of fault isolation available with various instrumentation suites. The model and
examples of these analyses will be described and the data architectures enumerated.
Flight tests of this system will validate the data architecture and provide real-time flight
profiles to be further analyzed in the laboratory.
Introduction
The rotorcraft community has supported research in health and usage monitoring systems
(HUMS) through both industry and government sponsored programs [1,2]. The main
emphasis to date has been reduction of vibration and, thus, reduction in maintenance
requirements. While high vibration loads are a major cause of wear and damage in
rotorcraft systems, it is important to monitor process parameters such as engine
temperature, oil temperature, oil pressure, and chip detection in addition to vibration for
complete real-time condition monitoring of the flight system. These parameters provide
health status and enable monitoring of the safety of critical systems. Monitoring of the
safe operating ranges of parameters such as these provides input to the caution/advisory
panel and other displays in the cockpit. Many of the displays are related due to
relationships among the physical parameters, but it is left to the pilot to recognize and
utilize these relationships in reasoning about the basic cause of caution/advisory lights.
The model-based reasoning approach has much to offer in addressing the problem of
failure identification. While current instrumentation and data analysis techniques provide
