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Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
Uppsala universitet.ORCID iD: 0000-0002-2014-1308
Uppsala University, Department of Information Technology, Division of Computing Science.
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2014 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 65, no 8, p. 1126-1135Article in journal (Refereed) Published
Abstract [en]

The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods. In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems. The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability

Place, publisher, year, edition, pages
2014. Vol. 65, no 8, p. 1126-1135
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-14778DOI: 10.1016/j.compind.2014.06.003ISI: 000342326300003Scopus ID: 2-s2.0-84929076117Local ID: e33b8410-1bff-46be-bf03-ebefa947bf02OAI: oai:DiVA.org:ltu-14778DiVA, id: diva2:987751
Projects
Fastelaboratoriet - VINNEXC
Note
Validerad; 2014; 20140619 (bjobac)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
In thesis
1. An Integrated Development Approach for Monitoring and Simulation to Predict Functional Product Availability
Open this publication in new window or tab >>An Integrated Development Approach for Monitoring and Simulation to Predict Functional Product Availability
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For nearly two decades, business models such as Functional Products have been in focus within research and of interest in the manufacturing industry. Functional product offers consist of hardware, software, service -support systems and management of operation which, when developed in an integrated manner, together provide the customer with an agreed-upon function with a specified level of availability. Compared to product-oriented sales, this type of business model can provide added value to customers, usually through an increase in the service content. Due to the total care commitment, offering Functional Products requires management of reliability and maintainability in order to meet the availability requirement of the function provided. The development of the Functional Product must include holistic analysis and prediction of the functional product availability performance to reduce technical and economic risks and ensure that the function is delivered according to contract. The research performed in this thesis presents an integrated development approach for monitoring and simulation to predict functional product availability. It is shown how the constituents of a functional product can be modelled in an integrated manner in order to simulate and predict functional product availability. A part of this modelling strategy is demonstrated through a simulation case example to show that is possible through this approach to evaluate the availability of different functional product designs. To support the development of the monitoring capability needed for availability simulations it is shown how it is possible to develop fault detection and diagnosis methods for fault detection systems based on data stream management systems. It is also shown how data stream forecasting can be used to predict failures due to faults occurring at short notice. Different fault detection methods have been developed, tested and evaluated on real industrial applications to verify applicability as queries on data streams, managed by data stream management systems. The results from these tests have been evaluated for their predictive performance and detection accuracy. Finally, methodological and technological approaches to monitoring and analysis in functional product development and similar business models to functional products are reviewed. The results showed that few research contributions address the information perspective in functional product development and similar business models holistically. The integrated development approach presented is a pragmatic approach to functional product development which is based on the merged research results of the papers included and knowledge domain presented.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2017
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-63826 (URN)978-91-7583-920-2 (ISBN)978-91-7583-921-9 (ISBN)
Public defence
2017-09-22, E632, Porsön Campus, Luleå, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2017-06-14 Created: 2017-06-09 Last updated: 2018-10-19Bibliographically approved

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Alzghoul, AhmadBacke, BjörnLöfstrand, Magnus

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