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
Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines
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)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed) Published
Abstract [en]

The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

Place, publisher, year, edition, pages
2018. Vol. 72, p. 1327-1332
Keywords [en]
population based maintenance, condition monitoring, automatic signal selection
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-15117DOI: 10.1016/j.procir.2018.03.208Scopus ID: 2-s2.0-85049577336OAI: oai:DiVA.org:his-15117DiVA, id: diva2:1203226
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-10-31Bibliographically 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, Lihui

Search in DiVA

By author/editor
Schmidt, BernardGandhi, KanikaWang, Lihui
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
In the same journal
Procedia CIRP
Reliability and Maintenance

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 114 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