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Modelización híbrida para el diagnóstico y pronóstico de fallos en el sector del transporte: Datos adquiridos y datos sintéticos
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2015 (Spanish)In: Dyna, ISSN 0012-7361, Vol. 90, no 2, 139-145 p.Article in journal (Refereed) Published
Abstract [en]

Safety in transport is a key. Railway and aerospace sectors have a need for ways to predict the behaviour of trains and aircraft, respectively. With this information, maintenance tasks for the correct operation of the assets can be carried out, reducing the number of failures that can cause an accident. However, the lack of enough data of the faulty state of those systems makes this to be difficult. Because of that either hidden faults or unknown faults can occur. As regulations in transport are very restrictive, components are usually substituted in early states of their degradation, which implies a loss of useful life of those components.In this article a methodology to overcome this limitation is presented. This methodology consists in the fusion of data obtained from two sources: data acquired from the real system, and synthetic data generated using physical models of the system. These physical models should be constructed in such a way that they can reproduce the main failure modes that can occur in the modelled system. This data fusion, that creates a hybrid model, not only allows to classify the condition of the system according to the aforementioned failure modes, but also to define new data that do not belong to any of those failure modes as a new failure mode, improving diagnosis and prognosis processes.

Place, publisher, year, edition, pages
2015. Vol. 90, no 2, 139-145 p.
Research subject
Operation and Maintenance
URN: urn:nbn:se:ltu:diva-4760DOI: 10.6036/7252Local ID: 2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807OAI: diva2:977634

Validerad; 2015; Nivå 2; 20150304 (urklet)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2016-10-19Bibliographically 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)
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 (pdf) (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: 2016-10-13Bibliographically approved

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