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Degradation Simulator for Infinite Horizon Controlled Linear Time-Invariant Systems
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Electronics.ORCID iD: 0000-0001-7340-4629
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Electronics.ORCID iD: 0000-0001-5429-7223
2023 (English)In: Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis, Springer , 2023, p. 155-164Chapter in book (Refereed)
Abstract [en]

Diagnosis, fault prediction, and Remaining Useful Life (RUL) estimation are among the predictive maintenance research subjects used for maintenance cost reduction. Using the available data with different machine learning methods, especially deep learning methods, the accuracy of estimation and prediction of faults and RUL have increased dramatically. However, due to the statistical nature of the machine learning methods and the limitations of available datasets, physically interpreting this information might be impossible. On the other hand, controlling the degradation and faults in the machines as the optimum predictive maintenance solution needs the physical interpretation of the method’s outcome. In order to test the new process-based methods for degradation and fault control, datasets with more information are required (compared to available datasets). In this article, we introduce an open-source degradation simulator for linear systems. This simulator can simulate the degradation in closed-loop machines whose dynamics are known. It is also possible to simulate different degradation models for different system parts simultaneously by adding different processes and output noise to the system. This simulator can generate enough data to test new machine learning-based predictive maintenance methods. 

Place, publisher, year, edition, pages
Springer , 2023. p. 155-164
Series
Studies in Systems, Decision and Control (SSDC), ISSN 2198-4182, E-ISSN 2198-4190 ; 467
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hig:diva-42668DOI: 10.1007/978-3-031-27540-1_14Scopus ID: 2-s2.0-85162200654ISBN: 978-3-031-27540-1 (electronic)ISBN: 978-3-031-27539-5 (print)OAI: oai:DiVA.org:hig-42668DiVA, id: diva2:1778622
Available from: 2023-07-03 Created: 2023-07-03 Last updated: 2023-07-03Bibliographically approved

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Hosseinzadeh Dadash, AmirhosseinBjörsell, Niclas

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CiteExportLink to record
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Citation style
  • apa
  • harvard-cite-them-right
  • ieee
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Language
  • sv-SE
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  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
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  • asciidoc
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