Prognostics

Description

Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as “predicting the time at which a component will no longer perform its intended function”. Loss of function is often times the time at which a component fails. The predicted time to that point becomes then the remaining useful life (RUL). For prognostics to be effective, it must be performed well before deviations from normal performance propagate to a critical effect. This enables a failure preclusion or prevention function to repair or replace the offending components, or if the components cannot be repaired, to retire the system (or vehicle) before the critical failure occurs. Therefore, prognosis has the promise to provide critical information to system operators that will enable safer operation and more cost-efficient use. To that end, Department of Defense (DoD), NASA, and industry have been investigating this technology for use in their vehicle health management solutions. Dedicated prognostic algorithms (in conjunction with failure detection and fault isolation algorithms) must be developed that are capable of operating in an autonomous and real-time vehicle health management system software architecture that is possibly distributed in nature. This envisioned prognostic and health management system will be realized in a vehicle-level reasoner that must have visibility and insight into the results of local diagnostic and prognostic technologies implemented at the LRU and subsystem levels. Accomplishing this effectively requires an integrated suite of prognostic technologies that compute failure effect propagation through diverse subsystems and that can capture interactions that occur in these subsystems. In this chapter a generic set of selected prognostic algorithm approaches is presented and an overview of the required vehicle-level reasoning architecture needed to integrate the prognostic information across systems is provided.

Resources

Name Format Description Link
33 chapter https://c3.nasa.gov/dashlink/static/media/publication/c17_2.pdf
21 Search results for publications that cite this dataset by its DOI. https://scholar.google.com/scholar?q=10.5067%2FPP14EED9ZOE2
21 NASA's newest search and order tool for subsetting, reprojecting, and reformatting data. https://search.earthdata.nasa.gov/search?q=SV16M_V+V001
21 Direct download via HTTPS protocol. https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD
21 NASA's newest search and order tool for subsetting, reprojecting, and reformatting data. https://search.earthdata.nasa.gov/search?q=SV16M_V+V001
21 Direct download via HTTPS protocol. https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD
21 NASA's newest search and order tool for subsetting, reprojecting, and reformatting data. https://search.earthdata.nasa.gov/search?q=SV16M_V+V001
21 Direct download via HTTPS protocol. https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD
21 Provides access to data, documentation, tools, citation information, support, and other resources. https://doi.org/10.5067/PP14EED9ZOE2
21 Includes a user's guide, supplemental documents like ATBDs and academic papers, How Tos, FAQs, etc. https://doi.org/10.5067/PP14EED9ZOE2
33 chapter https://c3.nasa.gov/dashlink/static/media/publication/c17_2.pdf

Tags

  • dashlink
  • nasa
  • ames

Topics

Categories