A sequential model-based approach for gas turbine performance diagnostics

Chen, Yu-Zhi; Zhao, Xudong; Xiang, Heng-Chao; Tsoutsanis, Elias

doi:10.1016/j.energy.2020.119657

Cited by 56 publications

(24 citation statements)

References 49 publications

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“…The driving solution algorithms of the nonlinear gas path diagnosis methods include local optimization algorithms (such as Newton-Raphson algorithm [13] and Kalman filter algorithm [14,15]) and global optimization algorithms (such as particle filter algorithm [16] or genetic algorithm [17,18]). To address the existing problems of low diagnostic accuracy lead by linearization of the thermodynamic system, and problems that sensor measurement noise and bias can cause large deviations in diagnostic accuracy, scholars have made considerable improvements [19][20][21]. However, when above-mentioned gas turbine nonlinear diagnostic methods are deployed in a gas turbine power plant, there are actually the following three difficulties:…”

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

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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

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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

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“…There are also methods developed where expected degradation relationships are taken into consideration in the analysis [10]. A sequential approach to GPA was explored in [11] where the state of the components was diagnosed one after another along the gas path. Another physics-based method is the Kalman Filter (KF) [12,13], where an estimate of the upcoming time-step is calculated based on previous observations.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Stenfelt

Kyprianidis

2022

Machines

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In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and requirements regarding what data transmit. Early in the design process, these parameters are relatively easy to change, compared to a mature product. If the gas turbine diagnostic system is not considered early in the design process, it may lead to diagnostic functions having to operate with reduced amount of data. In this paper, a scenario where the diagnostic function cannot obtain airplane installation effects is considered. The installation effects in question is air intake pressure loss (pressure recovery), bleed flow and shaft power extraction. A framework is presented where the unknown installation effects are estimated based on available data through surrogate models, which is incorporated into the diagnostic framework. The method has been evaluated for a low-bypass turbofan with two different sensor suites. It has also been evaluated for two different diagnostic schemes, both determined and underdetermined. Results show that, compared to assuming a best-guess constant-bleed and shaft power, the proposed method reduce the RMS in health parameter estimation from 26% up to 80% for the selected health parameters. At the same time, the proposed method show the same degradation pattern as if the installation effects were known.

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“…The civil aviation market has grown remarkably since its birth in the early 1950s, and it is predicted that the number of passengers will boom up before 2050 [1]. There is a growing body of literatures that recognize the importance of condition-based maintenance for the operation of aero engines [2]. The remaining useful life (RUL) of aero engines is a major area of interest within the field of condition-based maintenance.…”

Section: Introductionmentioning

confidence: 99%

Similarity-Based Echo State Network for Remaining Useful Life Prediction

Wang

Chen

Zhao

et al. 2022

J. Phys.: Conf. Ser.

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The health state of aero engines will affect economic benefits, availability, and safety of aircrafts. Fault prognosis is considered to be one of useful methods to evaluate the health state of aero engines and avoid unscheduled down time. Prognosis is the ability to forecast the evolution of engine deterioration. The two major objectives of prognosis are: forecasting the impending failures and predicting the engines’ remaining useful life (RUL). This paper proposes a method that combines echo state network (ESN) with similarity-based methods for RUL prediction. First, principal component analysis (PCA) is used to pre-process data to obtain degradation trajectories, which handles the fusion problem of multiple data. Second, in order to use historical information accurately, the Euclidean distance is applied to calculate the similarity among different degradation trajectories. Third, the trajectory with the highest similarity is used in the ESN training process. Then, a numerical simulation on the NASA C-MAPSS dataset verifies the accuracy of the method. Finally, the comparison with other methods shows the superiority of the method.

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A sequential model-based approach for gas turbine performance diagnostics

Cited by 56 publications

References 49 publications

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

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

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Similarity-Based Echo State Network for Remaining Useful Life Prediction

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