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Smart Maintenance using System Identification
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Sustainable development
The essay/thesis is mainly on sustainable development according to the University's criteria
Abstract [en]

This project discusses the use of System Identification for Smart Maintenance. System Identification is the process of finding a mathematical model of a system using empirical data. The mathematical model can then be used to detect and predict the maintenance needs, which is considered as Smart Maintenance. Smart maintenance strategies have gained pretty much importance recently, since it contributes to economically sustainable production. This project uses the LAVA-framework, proposed in [1] for non-linear system identification, which has the capability of explaining the dynamics of the system very well, and at the same time follows the principle of parsimony. A nominal model is first identified using data from a system that operates under normal operating conditions, then the identified nominal model is used to detect when the system starts to deviate from normal behavior, and these deviations indicate the deteriorations in the system. Furthermore, a new Multiple Model Method which is developed in [2] using the similar idea from LAVA, is applied on the large data set of a system that operates on separate batches and units, which identifies individual model for each batch and unit, which is then used to detect the deficient units or batches and changes in the system behavior. Finally, the proposed methods are applied to two different real world industrial cases; a Heat exchanger and a Wood Moulder Machine. In the first, the purpose is to detect the dirt in a Heat Exchanger, and in the second, the goal is to detect when the tool in a Wood Moulder Machine needs to be changed.

Place, publisher, year, edition, pages
2019. , p. 74
Keywords [en]
System Identification, Nonlinear Systems, Maintenance
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:hig:diva-30735OAI: oai:DiVA.org:hig-30735DiVA, id: diva2:1357931
Subject / course
Electronics
Educational program
Electronics/Automation – master's programme (two years) (sv or eng)
Supervisors
Examiners
Available from: 2019-10-09 Created: 2019-10-04 Last updated: 2019-10-09Bibliographically approved

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Department of Electrical Engineering, Mathematics and Science
Control Engineering

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Citation style
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