Failure prognosis based on adaptive state space models

Guangxing Bai, Amirmahyar Abdolsamadi, Pingfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a generic data-driven failure prognosis method based on adaptive state space models for engineering systems, which integrates adaptive model recognition with a dynamic system model for remaining useful life prediction. The developed approach employs a statistical learning framework for adaptively learning of time-series degradation performance, and then a Bayesian technique for self-updating of data-driven models to adapt the operational or environmental changes. With the developed approach, the prognosis technique can eliminate the dependence to system specific models and be adaptive to system performance changes due to degradation or variation of system operational conditions, thereby yielding accurate remaining useful life predictions. The developed methodology is demonstrated by an engineering case study.

Original languageEnglish (US)
Title of host publicationAdvances in Aerospace Technology
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791850510
DOIs
StatePublished - 2016
Externally publishedYes
EventASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016 - Phoenix, United States
Duration: Nov 11 2016Nov 17 2016

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume1

Conference

ConferenceASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016
CountryUnited States
CityPhoenix
Period11/11/1611/17/16

ASJC Scopus subject areas

  • Mechanical Engineering

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