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Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks
Energy Reports ( IF 5.2 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.egyr.2020.04.029
Adel Alblawi

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.

中文翻译:

基于热力学模型结合多前馈人工神经网络的工业燃气轮机故障诊断

摘要 在本文提出的研究中,使用热力学原理和多前馈人工神经网络 (MFANN) 系统模拟了工业燃气轮机在运行设计点期间性能的恶化。最初,热力学模型是使用组件性能图技术构建的,这需要计算符合每个组件性能图的工作点。通过改变发动机部件的效率或外部环境条件并模拟每种情况下的发动机性能,生成了各种设计操作点。在训练和测试阶段使用这些操作点构建MFANN模型。这样,两个MFANN模型就建立起来了。
更新日期:2020-11-01
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