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Cloud-enhanced predictive maintenance
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. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
2016 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed) Epub ahead of print
Abstract [en]

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

Place, publisher, year, edition, pages
Springer, 2016.
Keywords [en]
Predictive maintenance, Condition-based maintenance, Context awareness, Cloud manufacturing
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-12331DOI: 10.1007/s00170-016-8983-8OAI: oai:DiVA.org:his-12331DiVA, id: diva2:933621
Available from: 2016-06-07 Created: 2016-06-07 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

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Schmidt, BernardWang, Lihui

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