Prognostic hybrid model from data fusion on machine tools

Víctor Simón; Diego Galar

Prognostic hybrid model from data fusion on machine tools

Víctor Simón
, Diego Galar

Luleå University of Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper proposes an enhancement of RUL prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.

Original language	English
Publication status	Published - 2015
Externally published	Yes
Event	21st IMEKO World Congress on Measurement in Research and Industry - Prague, Czech Republic Duration: 30 Aug 2015 → 4 Sept 2015

Conference

Conference	21st IMEKO World Congress on Measurement in Research and Industry
Country/Territory	Czech Republic
City	Prague
Period	30/08/15 → 4/09/15

Keywords

Hybrid model
RUL
Trajectory tracking

Cite this

@conference{ec69bef182124e3bbd84a5a20f24c20b,

title = "Prognostic hybrid model from data fusion on machine tools",

abstract = "This paper proposes an enhancement of RUL prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.",

keywords = "Hybrid model, RUL, Trajectory tracking",

author = "V{\'i}ctor Sim{\'o}n and Diego Galar",

year = "2015",

language = "English",

note = "21st IMEKO World Congress on Measurement in Research and Industry ; Conference date: 30-08-2015 Through 04-09-2015",

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TY - CONF

T1 - Prognostic hybrid model from data fusion on machine tools

AU - Simón, Víctor

AU - Galar, Diego

PY - 2015

Y1 - 2015

N2 - This paper proposes an enhancement of RUL prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.

AB - This paper proposes an enhancement of RUL prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.

KW - Hybrid model

KW - RUL

KW - Trajectory tracking

UR - https://www.scopus.com/pages/publications/84951095353

M3 - Paper

AN - SCOPUS:84951095353

T2 - 21st IMEKO World Congress on Measurement in Research and Industry

Y2 - 30 August 2015 through 4 September 2015

ER -

Prognostic hybrid model from data fusion on machine tools

Abstract

Conference

Keywords

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Cite this