Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Synthetic data generation in hybrid modelling of rolling element bearings
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.ORCID iD: 0000-0003-4913-6438
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-8278-8601
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
IK4-Ikerlan.
2015 (English)In: Insight: Non-Destructive Testing & Condition Monitoring, ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, no 7, p. 395-400Article in journal (Refereed) Published
Abstract [en]

Diagnosis and prognosis processes are necessary to optimise the dependability of systems and ensure their safe operation. If there is a lack of information, faulty conditions cannot be identified and undesired events cannot be predicted. It is essential to predict such events and mitigate risks, but this is difficult in complex systems.Abnormal or unknown faults cause problems for maintenance decision makers. We therefore propose a methodology that fuses data-driven and model-based approaches. Real data acquired from a real system and synthetic data generated from a physical model can be used together to perform diagnosis and prognosis.As systems have time-varying conditions related to both the operating condi- tions and the healthy or faulty state of systems, the idea behind the proposed methodology is to generate synthetic data in the whole range of conditions in which a system can work. Thus, data related to the context in which the system is operating can be generated.We also take a first step towards implementing this methodology in the field of rolling element bearings. Synthetic data are generated using a physical model that reproduces the dynamics of these machine elements. Condition indicators such as root mean square, kurtosis and shape factor, among others, are calculated from the vibrational response of a bearing and merged with the real features obtained from the data collected from the functioning systemFinally, the merged indicators are used to train SVM classifiers (support vector machines), so that a classification according to the condition of the bearing is made independently of the applied loading conditions even though some of the scenarios have not yet occurred.

Place, publisher, year, edition, pages
2015. Vol. 57, no 7, p. 395-400
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-9277DOI: 10.1784/insi.2015.57.7.395ISI: 000358757800006Scopus ID: 2-s2.0-84936998655Local ID: 7de4b34a-b967-420e-aaa8-8bb99b07b89dOAI: oai:DiVA.org:ltu-9277DiVA, id: diva2:982215
Note
Validerad; 2015; Nivå 2; 20150506 (madmis)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2024-01-17Bibliographically approved
In thesis
1. Hybrid modelling in condition monitoring
Open this publication in new window or tab >>Hybrid modelling in condition monitoring
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Hybridmodellering inom tillståndsövervakning
Abstract [en]

Assuring the reliability, availability, maintainability and safety of assets is key to business success. A logical first step is to consider the requirements of assets in the design process. However, these concepts must also be assured during the assets’ operation. Consequently, it is important to have knowledge of their actual condition.

The condition monitoring of assets and their subsequent maintenance are changing with the rapid evolution of electronics and information and communication technologies. The contribution of such technologies to the monitoring of cyber-physical systems in the context of Industry 4.0 is important.

In the era of big data, the ease of getting, storing and processing data is crucial. However, the trend towards big data is not as effective in the field of condition monitoring as in others. One of the challenges of today’s condition monitoring is the lack of data on those assets not allowed to operate beyond their pre-established maintenance limit. Datasets miss advanced degradation states of assets and fail to predict rarely occurring outliers, but both have a great impact on operation; in other words, data-driven methods are limited and cannot accurately tackle scenarios outside the training dataset.

This thesis proposes augmenting such datasets with the addition of synthetic data generated by physics-based models describing the dynamic behaviour of assets. It argues a combination of physics-based and data-driven modelling, known as hybrid modelling, can overcome the aforementioned limitations. It proposes an architecture for hybrid modelling, based on data fusion and context awareness and oriented to diagnosis and prognosis.

The thesis applies some of the key parts of this architecture to rotating machinery, developing a physics-based model for a rotating machine from an electromechanical point of view and following a multi-body approach. It verifies and validates the model following guidelines suggested in the literature and using experimental data acquired in predefined tests with a commercial test rig.

The developed physics-based model is used to generate synthetic data in different degradation states, and these data are fused with condition monitoring data acquired from the test rig. A data-driven approach is used to train an algorithm with the resulting fused data, adapting the clusters obtained by an algorithm to the context in which the machine is operating. The hybrid model is applied specifically for fault detection, localisation and quantification. The use of context data is found to enhance the results and is the key to providing context-driven services in the future.

In short, the model is ready to react to faults that have not occurred in reality, with a severity that has not been reached in a specific operating context but has been introduced in the physics-based modelling.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2016
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-59652 (URN)978-91-7583-721-5 (ISBN)978-91-7583-722-2 (ISBN)
Public defence
2016-12-19, F1031, Luleå University of Technology, 971 82, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2016-10-12 Created: 2016-10-11 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Leturiondo, UrkoMishra, MadhavGalar, Diego

Search in DiVA

By author/editor
Leturiondo, UrkoMishra, MadhavGalar, Diego
By organisation
Operation, Maintenance and Acoustics
In the same journal
Insight: Non-Destructive Testing & Condition Monitoring
Other Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 511 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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