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Data stream forecasting for system fault prediction
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0002-2014-1308
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
2012 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 62, no 4, p. 972-978Article in journal (Refereed) Published
Abstract [en]

Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing.In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM).The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.

Place, publisher, year, edition, pages
2012. Vol. 62, no 4, p. 972-978
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-3462DOI: 10.1016/j.cie.2011.12.023ISI: 000303092500013Scopus ID: 2-s2.0-84858701770Local ID: 149beecc-8334-4068-aa81-11558268347fOAI: oai:DiVA.org:ltu-3462DiVA, id: diva2:976320
Projects
Fastelaboratoriet - VINNEXC
Note
Validerad; 2012; 20111230 (ysko)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, AhmadLöfstrand, MagnusBacke, Björn

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