A Survey of Artificial Intelligence for Prognostics

Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.

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  • National Provider
  • North America
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  • north-america
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license_id us-pd
license_title us-pd
maintainer Miryam Strautkalns
maintainer_email miryam.strautkalns@nasa.gov
metadata_created 2025-11-29T18:13:37.515902
metadata_modified 2025-11-29T18:13:37.515906
notes Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.
num_resources 1
num_tags 8
title A Survey of Artificial Intelligence for Prognostics