his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Context preparation for predictive analytics – a case from manufacturing industry
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-8906-630X
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)
School of Engineering Science, Kungliga Tekniska Högskolan, Stockholm, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
Department of Civil, Environmental and Natural Resources Engineering, Luleå Tekniska Universitet, Luleå, Sweden.ORCID iD: 0000-0002-4107-0991
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

Design/methodology/approach

This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

Findings

Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

Originality/value

This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

Place, publisher, year, edition, pages
2017. Vol. 23, no 3, p. 341-354
Keywords [en]
Predictive maintenance, Context awareness, Condition monitoring
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-14033DOI: 10.1108/JQME-10-2016-0050ISI: 000412478700007Scopus ID: 2-s2.0-85027991065OAI: oai:DiVA.org:his-14033DiVA, id: diva2:1135744
Available from: 2017-08-24 Created: 2017-08-24 Last updated: 2018-05-02Bibliographically approved
In thesis
1. Toward Predictive Maintenance in a Cloud Manufacturing Environment: A population-wide approach
Open this publication in new window or tab >>Toward Predictive Maintenance in a Cloud Manufacturing Environment: A population-wide approach
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The research presented in this thesis is focused on improving industrial maintenance by using better decision support that is based on a wider range of input information. The core objective is to research how to integrate information from a population of similar monitored objects. The data to be aggregated comes from multiple disparate sources including double ball-bar circularity tests, the maintenance management system, and the machine tool’s controller. Various data processing and machine learning methods are applied and evaluated. Finally, an economic evaluation of the proposed approach is presented. The work performed is presented in five appended papers.

Paper I presents an investigation of cloud-based predictive maintenance concepts and their potential benefits and challenges.

Paper II presents the results of an investigation of available and potentially useful data from the perspective of predictive analytics with a focus on the linear axes of machine tools.

Paper III proposes a semantic framework for predictive maintenance, and investigates means of acquiring relevant information from different sources (i.e., ontology-based data retrieval).

Paper IV presents a method for data integration. The method is applied to data obtained from a real manufacturing setup. Simulation-based evaluation is used to compare results with a traditional time-based approach.

Paper V presents the results from additional simulation-based experiments based on the method from Paper IV. The aim is to improve the method and provide additional information that can support maintenance decision-making (e.g., determining the optimal interval for inspections).

The method developed in this thesis is applied to a population of linear axes from a set of similar multipurpose machine tools. The linear axes of machine tools are very important, as their performance directly affects machining quality. Measurements from circularity tests performed using a double ball-bar measuring device are combined with event and context information to build statistical failure and classification models. Based on those models, a decision-making process is proposed and evaluated. In the analysed case, the proposed approach leads to direct maintenance cost reduction of around 40 % compared to a time-based approach.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2018
Series
Dissertation Series ; 20 (2018)
Keywords
predictive maintenance, condition monitoring, population-wide approach, machine learning, double ball-bar measurement
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15120 (URN)978-91-984187-2-9 (ISBN)
Public defence
2018-05-21, Insikten, Kanikegränd 3A, Skövde, 10:00 (English)
Opponent
Supervisors
Projects
IPSI Research School
Available from: 2018-05-03 Created: 2018-05-02 Last updated: 2018-05-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Schmidt, BernardGandhi, KanikaWang, LihuiGalar, Diego

Search in DiVA

By author/editor
Schmidt, BernardGandhi, KanikaWang, LihuiGalar, Diego
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
In the same journal
Journal of Quality in Maintenance Engineering
Reliability and Maintenance

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 253 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf