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Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.ORCID iD: 0000-0003-4913-6438
IK4-Ikerlan.ORCID iD: 0000-0002-5656-2534
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Number of Authors: 3
(English)In: Structural Health Monitoring, ISSN 1475-9217, E-ISSN 1741-3168Article in journal (Refereed) Accepted
Abstract [en]

Diagnosis and prognosis are key issues in the application of condition based maintenance. Thus, there is a need to evaluate the condition of a machine. Physics-based models are of great interest as they give the response of a modelled system in different operating conditions. This strategy allows the generation of synthetic data that can be used in combination with real data acquired by sensors to improve maintenance. The paper presents an electromechanical model for a rotating machine, with special emphasis on the modelling of rolling element bearings. The proposed model is validated by comparing the simulation results and the experimental results in different operating conditions and different damaged states. This comparison shows good agreement, obtaining differences up to 10 % for the modelling of the whole rotating machine and less than 0.6 % for the model of the bearing.

Keyword [en]
condition based maintenance, diagnosis, physics-based modelling, rotating machine, rolling element bearing, experimental validation, vibration, damage
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
URN: urn:nbn:se:ltu:diva-59537OAI: diva2:1033211
Available from: 2016-10-06 Created: 2016-10-06 Last updated: 2016-11-22
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
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
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)
Available from: 2016-10-12 Created: 2016-10-11 Last updated: 2016-11-24Bibliographically approved

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