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Toward Predictive Maintenance in a Cloud Manufacturing Environment: A population-wide approach
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
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 [en]
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: urn:nbn:se:his:diva-15120ISBN: 978-91-984187-2-9 (print)OAI: oai:DiVA.org:his-15120DiVA, id: diva2:1203231
Public defence
2018-05-21, Insikten, Kanikegränd 3A, Skövde, 10:00 (English)
Opponent
Supervisors
Projects
IPSI Research SchoolAvailable from: 2018-05-03 Created: 2018-05-02 Last updated: 2018-05-03Bibliographically approved
List of papers
1. Cloud-enhanced predictive maintenance
Open this publication in new window or tab >>Cloud-enhanced predictive maintenance
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
Predictive maintenance, Condition-based maintenance, Context awareness, Cloud manufacturing
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12331 (URN)10.1007/s00170-016-8983-8 (DOI)
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2018-05-02Bibliographically approved
2. Context preparation for predictive analytics – a case from manufacturing industry
Open this publication in new window or tab >>Context preparation for predictive analytics – a case from manufacturing industry
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.

Keywords
Predictive maintenance, Context awareness, Condition monitoring
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14033 (URN)10.1108/JQME-10-2016-0050 (DOI)000412478700007 ()2-s2.0-85027991065 (Scopus ID)
Available from: 2017-08-24 Created: 2017-08-24 Last updated: 2018-05-02Bibliographically approved
3. Semantic Framework for Predictive Maintenance in a Cloud Environment
Open this publication in new window or tab >>Semantic Framework for Predictive Maintenance in a Cloud Environment
2017 (English)In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, p. 583-588Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Procedia CIRP, ISSN 2212-8271 ; 62
Keywords
Predictive maintenance, Knowledge management, Cloud manufacturing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13568 (URN)10.1016/j.procir.2016.06.047 (DOI)000414525400101 ()2-s2.0-85020714569 (Scopus ID)
Conference
10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '16, Ischia, Italy, 20-22 July 2016
Available from: 2017-05-19 Created: 2017-05-19 Last updated: 2018-05-02Bibliographically approved
4. Integration of events and offline measurement data from a population of similar entities for condition monitoring
Open this publication in new window or tab >>Integration of events and offline measurement data from a population of similar entities for condition monitoring
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy.

Keywords
condition monitoring, population-wide data, double ball-bar measurement, feature selection, machine learning
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15116 (URN)
Note

to be submitted to:

International Journal of Computer Integrated ManufacturingSpecial Issue on Smart Cyber-Physical System Applications in Production and Logistics

Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-05-25
5. Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines
Open this publication in new window or tab >>Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines
(English)Manuscript (preprint) (Other academic)
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.

Keywords
population based maintenance, condition monitoring, automatic signal selection
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15117 (URN)
Note

Accepted for 51st CIRP Conference on Manufacturing Systems

Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-05-25

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