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A NEW HYBRID PROGNOSTIC METHODOLOGY

A NEW HYBRID PROGNOSTIC METHODOLOGY

Authors :
Omer F. Eker
Fatih Camci
Ian K. Jennions
Source :
International Journal of Prognostics and Health Management, Vol 10, Iss 2 (2019)
Publication Year :
2019
Publisher :
The Prognostics and Health Management Society, 2019.

Abstract

Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.

Details

Language :
English
ISSN :
21532648
Volume :
10
Issue :
2
Database :
Directory of Open Access Journals
Journal :
International Journal of Prognostics and Health Management
Publication Type :
Academic Journal
Accession number :
edsdoj.6919f496c2a34e178380ca6e86e5320e
Document Type :
article
Full Text :
https://doi.org/10.36001/ijphm.2019.v10i2.2727