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

机译：基于热力学模型的工业燃气轮机的故障诊断与多馈人工神经网络相结合

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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.
机译：在本文提出的研究中，通过使用热力学原理和多馈人工神经网络（MFANN）系统模拟了在操作设计点期间的工业燃气轮机性能的劣化。最初使用组件性能图技术构造了热力学模型，该技术需要计算符合每个组件的性能图的操作点。通过改变发动机组件的效率或外部环境条件并模拟每种情况的发动机性能来产生各种设计操作点。 MFANN模型是通过使用这些操作点进行培训和测试阶段的构造。通过这种方式，建立了两个MFANN模型。第一款模型的目的是计算发动机的性能，而第二种模型用于检测发动机部件的劣化本文呈现出燃气涡轮机劣化检测的强大故障诊断系统，目的是提高能量效率。

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