Robust Adaptive Fault Reconfiguration for Micro-gas Turbine Based on Optimized T–S Fuzzy Model and Nonsingular TSMO

Ma, Leiming; Xiao, Lingfei; Meng, Zhongxiang; Huang, Xinhao

doi:10.1007/s40815-020-00917-7

Cited by 6 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…c. For users of gas turbine power plants, another practical problem is that users often do not have any gas turbine thermodynamic modeling technology, let alone a thermodynamic model decision-making based diagnostic technology. For the existing data-driven artificial intelligence diagnosis methods that are commonly used, such as neural networks [22][23][24][25], and fuzzy logic [26][27][28][29], as illustrated in the fig. 2, it is often necessary to build on an existing component fault data sample set.…”

Section: Fig1 Thermodynamic Model Decision-making Based Gas-path Diagnosis Methodsmentioning

confidence: 99%

Gas Path Fault Diagnosis of Gas Turbine Engine Based on Knowledge Data-Driven Artificial Intelligence Algorithm

Jin

Ying

et al. 2021

IEEE Access

View full text Add to dashboard Cite

As the core power for the aviation industry, shipbuilding industry, and power station industry, it is essential to ensure that the gas turbines operate safely, reliably, greenly and efficiently. Learn from the advantages and disadvantages of the thermodynamic model based and data-driven artificial intelligence based gas-path diagnosis methods, a newfangled gas turbine gas-path diagnosis approach on the basis of knowledge data-driven artificial intelligence is proposed. That is a hybrid method of deep learning and gas path analysis. First, gas turbine thermodynamic model of the object to be diagnosed is constructed by adaptation modeling strategy. And the engine thermodynamic model is taken as the basal model to simulate various gas path faults. Secondly, a large number of knowledge data corresponding to component health parameters and gas turbine boundary condition parameters & gas-path measurable parameters are simulated by setting different component health parameter values and different boundary conditions based on this basal model. And next, define the vector composed of the boundary condition parameters & the gas path measurable parameters in the knowledge database as the input vector, and the component health parameter vector as the output vector, and a deep learning model for regression modeling of this knowledge database is designed. At last, along with the gas turbine engine runs, the trained model outputs component health parameters in real time after trained deep learning model is deployed to the corresponding gas turbine power plant. The simulation experiment results show that, accurate and quantified health parameters of each gas path component can be obtained by the proposed method in this paper, and the overall root mean square error does not exceed 0.033%, and the maximum relative error does not exceed 0.36%, which illustrates the proposed method has great application potential. INDEX TERMSGas turbine; thermodynamic model; component health parameter; gas path analysis; deep learning; SYMBOLS AND ABBREVIATIONS GPA Gas path analysis EOH Equivalent operating hour GRNN Generalized regression neural network BP Back propagation neural network RBF Radial basis function neural network DNN Deep learning model ANN Artificial neural network FC Fully connected layer SF Component health parameters u Boundary condition parameters Z Gas-path measurable parameters

show abstract