LOADING...
Text Size
Prognostics and Health Management for Faulted Aerospace Components
 

The Prognostics Center of Excellence (PCoE) at Ames Research Center provides an umbrella for prognostic technology development, specifically addressing prognostic technology gaps within the application areas of aeronautics and space exploration.

Prognostics focuses on predicting the health state assessment and remaining life estimation of faulted, aged, or worn components that are part of safety critical aerospace equipment

Diagnostic equipment

The effort covers projects from several mission directorates that are concerned with performing integrated system health management to contain, prevent, detect, diagnose, predict, respond to, and recover from conditions that may interfere with nominal system operations.

The projects investigate damage propagation mechanisms on select safety-critical actuators for transport-class aircraft, damage mechanisms on aircraft wiring insulation, and damage propagation mechanisms for critical electrical and electronic components in avionic equipment.Data collected from aging testbeds are used to validate the algorithms and to fine tune their performance. The common thread among the various avenues of health management technology development is the investigation of physics-of-failure at the component level. Modeling damage initiation and propagation at this level is a key element in describing component health. Just as important is the investment of resources into algorithm development to provide the estimates for remaining component life and for uncertainty management.

Some of the challenges that have been investigated include:

  • Uncertainty management: How can the information from multiple uncertainty sources be properly captured and processed?
  • Autonomic control reconfiguration: How can local prognostic information be translated into changes at the controller level such that controller objectives are satisfied in the long term?
  • Validation and verification: How can the proper operation of health management algorithms be validated, especially on new systems?
  • Post-prognostic reasoning: How can the information coming from a health reasoner be turned into an action, also factoring in other considerations such as logistics information, mission information, and fleet management?

Diagram of actual test and simulation
 

Image Token: 
[image-47]
Image Token: 
[image-62]
Page Last Updated: August 5th, 2013
Page Editor: NASA Administrator