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A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. (Digital Platforms)ORCID iD: 0000-0002-9890-4918
Fraunhofer, Germany.
Mälardalen University, Sweden.
Micro Turbine Technology BV, Netherlands.
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2021 (English)In: Energy and AI, ISSN 2666-5468, Vol. 4, article id 100064Article in journal, Editorial material (Refereed) Published
Abstract [en]

Predictive health monitoring of micro gas turbines can significantly increase the availability and reduce the operating and maintenance costs. Methods for predictive health monitoring are typically developed for large-scale gas turbines and have often focused on single systems. In an effort to enable fleet-level health monitoring of micro gas turbines, this work presents a novel data-driven approach for predicting system degradation over time. The approach utilises operational data from real installations and is not dependent on data from a reference system. The problem was solved in two steps by: 1) estimating the degradation from time-dependent variables and 2) forecasting into the future using only running hours. Linear regression technique is employed both for the estimation and forecasting of degradation. The method was evaluated on five different systems and it is shown that the result is consistent (r>0.8) with an existing method that computes corrected values based on data from a reference system, and the forecasting had a similar performance as the estimation model using only running hours as an input.

Place, publisher, year, edition, pages
2021. Vol. 4, article id 100064
Keywords [en]
Fleet monitoring, Micro gas turbine, Machine learning, Health monitoring, Predictive maintenance, Power generation
National Category
Energy Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-52673DOI: 10.1016/j.egyai.2021.100064OAI: oai:DiVA.org:ri-52673DiVA, id: diva2:1541491
Projects
FUDIPO
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EU, Horizon 2020Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2021-06-07Bibliographically approved

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Olsson, Tomas

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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