Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244103 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 6 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2020 [Pages:] 1083-1096
Publisher: 
Elsevier, Amsterdam
Abstract: 
In the study presented in this paper, the deterioration in the performance of an industrial gas turbine during the operation design point was simulated by using the thermodynamic principle and a multi feedforward artificial neural networks (MFANN) system. Initially the thermodynamic model was constructed using the components performance map technique, that entailed calculating the operating point which was compliant with the performance map for each component. The various design operation points were generated by changing the engine component's efficiency or outer environmental conditions and simulating the engine's performance for each case. The MFANN model was constructed by using these operation points for the training and testing stage. In this way, the two MFANN models were established. The aim of the first model was to calculate the engine's performance while the second model was used to detect the deterioration of the components of the engine This paper presents a robust fault diagnosis system for gas turbine degradation detection with the aim of improving energy efficiency.
Subjects: 
Energy efficiency
Engine performance and deterioration
Fault diagnosis
Industrial gas turbine
Multi feedforward artificial neural network
Thermodynamic model
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
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