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A MODEL-BASED SOLUTION FOR GAS TURBINE DIAGNOSTICS: SIMULATIONS AND EXPERIMENTAL VERIFICATION
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (Future Energy Center)ORCID iD: 0000-0001-6101-2863
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Siemens Industrial Turbomachinery AB, Sweden.
Siemens Industrial Turbomachinery AB, Sweden.
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2019 (English)In: Proceedings of the ASME Turbo ExpoVolume 6, 2019, 2019, article id GT2019-90858Conference paper, Published paper (Refereed)
Abstract [en]

Prompt detection of incipient faults and accurate monitoring of engine deterioration are key aspects for ensuring safe operations and planning a timely maintenance. Modern computing capabilities allow for more and more complex tools for engine monitoring and diagnostics. Nevertheless, an underlying physics-based approach is often preferable, because not only the “what” but also the “why” can be identified, providing an effective decision support tool to the service engineer. In this work, a physics-based adaptive model is used to evaluate performance deltas and correct the data to reference conditions (gas turbine load and ambient conditions), while a data-driven correlation algorithm identifies the most likely matches within a fault signatures database. Possible faults are ordered from the highest correlation in the decision support system and the most likely fault can be selected based on the number of occurrences and the associated correlation. Gradual engine degradation can also be monitored by displaying performance deltas trends during time. The diagnostics tool was tested on a validated performance model of a single-shaft industrial gas turbine and subsequently on experimental data. This paper presents the diagnostics system structure, the model adaptation scheme, and the results obtained from simulated and real fault data. Accurate fault isolation and severity identification were achieved in all cases, demonstrating the tool capability for decision support system.

Place, publisher, year, edition, pages
2019. article id GT2019-90858
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-45905DOI: 10.1115/GT2019-90858Scopus ID: 2-s2.0-85075526028ISBN: 9780791858677 (print)OAI: oai:DiVA.org:mdh-45905DiVA, id: diva2:1367677
Conference
ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition, GT 2019; Phoenix; United States; 17 June 2019 through 21 June 2019; Code 154121
Projects
DIAGNOSISAvailable from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-12-02Bibliographically approved

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Zaccaria, ValentinaStenfelt, MikaelKyprianidis, Konstantinos

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  • apa
  • ieee
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