A fault/anomaly system prognosis using a data-driven approach considering uncertainty

Author

Escobet, Teresa ; Quevedo, Joseba ; Puig, Vicenç

Author_Institution

DiPSE Dept., Univ. Politec. de Catalunya, Manresa, Spain

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

7

Abstract

This paper presents a data-driven prognostic strategy for failure prediction and computing the remaining useful life (RUL) using an autoregressive (AR) model combined with the recursive least squares (RLS) algorithm. The proposed method not only provides an estimation of the remaining useful life (RUL), but also a confidence interval based on modeling the uncertainty as a probabilistic Gaussian variable. To illustrate the performance of the proposed approach, a conveyor belt system that uses an AC electric motor to move a cart from one end to the other is used.

Keywords

AC motors; Gaussian processes; autoregressive processes; belts; conveyors; fault diagnosis; least squares approximations; probability; remaining life assessment; AC electric motor; anomaly system prognosis; autoregressive model; confidence interval; conveyor belt system; data-driven prognostic strategy; failure prediction; fault system prognosis; probabilistic Gaussian variable; recursive least squares algorithm; remaining useful life estimation; Belts; Degradation; Estimation; Mathematical model; Prediction algorithms; Predictive models; Uncertainty; data-driven approaches; prognosis; remaining useful life; uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252688

Filename

6252688