Novel RUL prediction of assets based on the integration of auto-regressive models and an RUSBoost classifier

Georgios Fagogenis, David Flynn, David Michael Lane

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

3 Citations (Scopus)

Abstract

This paper presents a novel, data-driven algorithm for the computation of the Remaining Useful Life (RUL) of an asset. The algorithm utilizes the asset’s state history to learn a prognostic model from data. The prognostic model comprises an ensemble of Auto-Regressive (AR) models, together with a state- of-the-art classifier. The AR part of the algorithm is used to predict the system’s state evolution. The classifier discriminates between healthy and faulty operation, given the asset’s current state. The predicted state, as computed by the AR model, is fed to the classifier. The first time when the predicted state is classified as faulty is returned as the RUL of the system. The resulting prognostic algorithm was tested on the CMAPSS dataset as provided from NASA Ames Research Center. Cases of unknown future input trajectory as well as cases with multiple faults have been investigated.
Original languageEnglish
Title of host publication2014 IEEE Conference on Prognostics and Health Management (PHM)
PublisherIEEE
ISBN (Print)9781479959426
DOIs
Publication statusPublished - 9 Feb 2015
Event2014 International Conference on Prognostics and Health Management - Cheney, United States
Duration: 22 Jun 201425 Jun 2014

Conference

Conference2014 International Conference on Prognostics and Health Management
Abbreviated titlePHM 2014
CountryUnited States
CityCheney
Period22/06/1425/06/14

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Software
  • Health Information Management

Fingerprint Dive into the research topics of 'Novel RUL prediction of assets based on the integration of auto-regressive models and an RUSBoost classifier'. Together they form a unique fingerprint.

  • Cite this