Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming

Safiyullah, Ferozkhan; Sulaiman, Shaharin Anwar; Naz, Misbah; Jasmani, Mohd Shahrizal; Ghazali, Syed Muhammad Afdhal

doi:10.1016/j.energy.2018.06.051

Cited by 32 publications

(12 citation statements)

References 19 publications

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“…Because the diagnostic accuracy of the small deviation linearization diagnosis methods is greatly affected by the variation of the boundary conditions (environmental conditions and operating conditions) and the sensor measurement noise, the nonlinear diagnosis methods are the mainstream of research. 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].…”

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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“…Talebi and Tousi (2017) [5] demonstrated that gas path analysis 75 (GPA), introduced by Urban (1969) [6], remains one of the soundest technologies for engine health monitoring and is 76 widely used for gas turbine condition monitoring to detect, identify, and assess component degradation. This, in turn, 77 affects the maintenance of gas turbine assets [7]. 78…”

Section: Take Down Policymentioning

confidence: 99%

A sequential model-based approach for gas turbine performance diagnostics

Chen

Zhao

Xiang

et al. 2021

Energy

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“…Based on the failure analysis of the compressor (Section 4.3) and the literature on compressor maintenance activities (Safiyullah et al, 2018), inspection and maintenance measures are proposed based on the performance degradation of the compressors and the drop in the volumetric flow rate.…”

Section: Inspection/maintenance Measures For Compressor Stationsmentioning

confidence: 99%

System-level prognosis and health monitoring modeling framework and software implementation for gas pipeline system integrity management

Chalgham

Mosleh

2020

Journal of Natural Gas Science and Engineering

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Developed the predictive physics of failure models and system-level pipeline health monitoring methodology. The project involves a multi-disciplinary science, engineering, and operational approach to realize a comprehensive and state-of-the-art solution to pipeline integrity. The PSIM platform integrates real-time and historical data, methods, predictive failure models, and systemlevel inference algorithms to form a total system health management support tool to aid in integrity decision making and planning by the pipeline operators. The approach is innovative and unique in its comprehensive integrative perspective and focuses on providing practical solutions while advancing the critical scientific and engineering foundations.

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Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming

Cited by 32 publications

References 19 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

A sequential model-based approach for gas turbine performance diagnostics

System-level prognosis and health monitoring modeling framework and software implementation for gas pipeline system integrity management

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