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Gas turbine prognostics via Temporal Fusion Transformer
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
2024 (English)In: Aeronautical Journal, ISSN 0001-9240Article in journal (Refereed) Epub ahead of print
Abstract [en]

Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

Place, publisher, year, edition, pages
CAMBRIDGE UNIV PRESS , 2024.
Keywords [en]
gas turbines prognostics, remaining useful life, Temporal Fusion Transformer, compressor washing, predictive maintenance, maintenance optimisation
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-66545DOI: 10.1017/aer.2024.40ISI: 001207525400001Scopus ID: 2-s2.0-85191409334OAI: oai:DiVA.org:mdh-66545DiVA, id: diva2:1856808
Available from: 2024-05-08 Created: 2024-05-08 Last updated: 2024-05-08Bibliographically approved

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Fentaye, Amare DesalegnKyprianidis, Konstantinos

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