Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks

• Main Author: Adel Alblawi
• Format: Article
• Language: English
• Published: Elsevier 2020-11-01
• Series: Energy Reports
• Subjects: Energy efficiency; Multi feedforward artificial neural network; Industrial gas turbine; Engine performance and deterioration; Thermodynamic model; Fault diagnosis
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352484720301177

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.