Semantic Framework for Predictive Maintenance in a Cloud Environment

Bernard Schmidt; Lihui Wang; Diego Galar

doi:10.1016/j.procir.2016.06.047

Semantic Framework for Predictive Maintenance in a Cloud Environment

Bernard Schmidt^*
, Lihui Wang
, Diego Galar

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

51 Citations (Scopus)

Abstract

Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

Original language	English
Pages (from-to)	583-588
Number of pages	6
Journal	Procedia CIRP
Volume	62
DOIs	https://doi.org/10.1016/j.procir.2016.06.047
Publication status	Published - 2017
Externally published	Yes
Event	10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 2016 - Ischia, Italy Duration: 20 Jul 2016 → 22 Jul 2016

Keywords

Cloud manufacturing
Knowledge management
Predictive maintenance

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.procir.2016.06.047

Cite this

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title = "Semantic Framework for Predictive Maintenance in a Cloud Environment",

abstract = "Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.",

keywords = "Cloud manufacturing, Knowledge management, Predictive maintenance",

author = "Bernard Schmidt and Lihui Wang and Diego Galar",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier B.V.; 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 2016 ; Conference date: 20-07-2016 Through 22-07-2016",

year = "2017",

doi = "10.1016/j.procir.2016.06.047",

language = "English",

volume = "62",

pages = "583--588",

journal = "Procedia CIRP",

issn = "2212-8271",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Semantic Framework for Predictive Maintenance in a Cloud Environment

AU - Schmidt, Bernard

AU - Wang, Lihui

AU - Galar, Diego

PY - 2017

Y1 - 2017

N2 - Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

AB - Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

KW - Cloud manufacturing

KW - Knowledge management

KW - Predictive maintenance

UR - https://www.scopus.com/pages/publications/85020714569

U2 - 10.1016/j.procir.2016.06.047

DO - 10.1016/j.procir.2016.06.047

M3 - Conference article

AN - SCOPUS:85020714569

SN - 2212-8271

VL - 62

SP - 583

EP - 588

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 2016

Y2 - 20 July 2016 through 22 July 2016

ER -

Semantic Framework for Predictive Maintenance in a Cloud Environment

Abstract

Keywords

UN SDGs

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