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Hybrid modelling in condition monitoring
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.ORCID iD: 0000-0003-4913-6438
2016 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Hybridmodellering inom tillståndsövervakning (Swedish)
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
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-59652ISBN: 978-91-7583-721-5ISBN: 978-91-7583-722-2 (pdf)OAI: oai:DiVA.org:ltu-59652DiVA: diva2:1034053
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: 2016-11-24Bibliographically approved
List of papers
1. Modelización híbrida para el diagnóstico y pronóstico de fallos en el sector del transporte: Datos adquiridos y datos sintéticos
Open this publication in new window or tab >>Modelización híbrida para el diagnóstico y pronóstico de fallos en el sector del transporte: Datos adquiridos y datos sintéticos
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.

Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-4760 (URN)10.6036/7252 (DOI)2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807 (Local ID)2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807 (Archive number)2bfd6141-3c2e-4fd0-8e23-6ca21e2f7807 (OAI)
Note

Validerad; 2015; Nivå 2; 20150304 (urklet)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2016-10-19Bibliographically approved
2. Methodology for the physics-based modelling of multiple rolling element bearing configurations
Open this publication in new window or tab >>Methodology for the physics-based modelling of multiple rolling element bearing configurations
2016 (English)In: Proceedings of the Institution of mechanical engineers. Proceedings part K, journal of multi-body dynamics, ISSN 1464-4193, E-ISSN 2041-3068Article in journal (Refereed) Epub ahead of print
Abstract [en]

Condition-based maintenance is a maintenance strategy which can be employed for monitoring the condition of rolling element bearings (REBs). For that purpose, the physics-based modelling of these machine elements is an interesting approach. There is a wide range of REBs regarding their internal configuration, dimensions and operating conditions. In this paper, a methodology to create a physics-based mathematical model to reproduce the dynamics of multiple kinds of REB is presented. Following a multi-body modelling, the proposed methodology takes advantage of the reusability of models to cover a wide range of bearing configurations, as well as to generalise the dimensioning of the bearing and the application of the operating conditions. The methodology is proved to be valid by its application to two case studies. Simulations of a deep-groove ball bearing and a cylindrical roller bearing are carried out, analysing their dynamic response as well as analysing the effects of damage in their parts. Results of the two case studies show good agreement with experimental data and results of other models in literature.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-2576 (URN)10.1177/1464419316660930 (DOI)0356284f-3562-4805-8311-8464ee10562a (Local ID)0356284f-3562-4805-8311-8464ee10562a (Archive number)0356284f-3562-4805-8311-8464ee10562a (OAI)
Note

Konferensartikel i tidskrift

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2016-12-03
3. Synthetic data generation in hybrid modelling of rolling element bearings
Open this publication in new window or tab >>Synthetic data generation in hybrid modelling of rolling element bearings
2015 (English)In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, no 7, 395-400 p.Article 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.

Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-9277 (URN)10.1784/insi.2015.57.7.395 (DOI)7de4b34a-b967-420e-aaa8-8bb99b07b89d (Local ID)7de4b34a-b967-420e-aaa8-8bb99b07b89d (Archive number)7de4b34a-b967-420e-aaa8-8bb99b07b89d (OAI)
Note
Validerad; 2015; Nivå 2; 20150506 (madmis)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2016-10-13Bibliographically approved
4. Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis
Open this publication in new window or tab >>Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis
(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
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
Identifiers
urn:nbn:se:ltu:diva-59537 (URN)
Available from: 2016-10-06 Created: 2016-10-06 Last updated: 2016-11-22

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