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Meeting	ACGS Committee Meeting 96 - Hilton Head - October 2005
Agenda Location	7 SUBCOMMITTEE E - FLIGHT, PROPULSION AND AUTONOMOUS VEHCILE CONTROL SYSTEMS 7.4 Techniques and Engineering Software for Prognostics and Health Management of Flight Control Actuators
Title	Techniques and Engineering Software for Prognostics and Health Management of Flight Control Actuators
Presenter	Carl Byington
Affiliation	Impact Technologies
Available Downloads*	presentation
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Abstract	Actuator health monitoring of military systems is currently performed both on-board the aircraft (Operational level) and at ground-based maintenance installations (i.e., the Intermediate or Depot level). Current on-board health monitoring employs Built-In-Tests (BITs), which apply conservative thresholds to flight control data to identify problems early and avoid in-flight failures. However, the extreme operation and interdependent nature of these military systems often causes flight control parameters to exceed these thresholds even though the component is healthy. Intermediate or Depot testing may fail to recreate the problems experienced or not substantiate the on-board assessment. This current approach has led to high incidences of Can Not Duplicate (CND), high sparing requirements, as well as affecting maintenance costs and operational readiness. The authors’ are developing Control Actuator Health Management (CAHM™) software that employs both data-driven and model-based algorithms for hydraulic servovalves and mechanical actuators. These software modules use actuator command/response data, physical actuator models, signal processing techniques, neural network modeling, advanced feature and knowledge fusion strategies, classification algorithms, and an evolving array of prognostics methods. Each class of diagnostic algorithm has specific requirements and advantages including the low computational burden of data-driven algorithms (when compared with other diagnostic techniques) and the ability of model based algorithms to relate faults back to physically meaningful parameters. These diagnostic algorithms will not only help isolate faults, but also provide a gray scale health assessment of components, which will be much more useful than a simple pass/fail BIT designation. In the presence of sufficient historical health information, diagnosis can be extended to prognosis using Impact’s suite of trending algorithms and novel tracking methods, which provide an estimate of remaining useful life (RUL). Impact will demonstrate the current state of the innovative software tool initially targeted towards implementation on an intermediate test data environment.