Compressor performance modelling method based on support vector machine nonlinear regression algorithm

Ying, Yulong; Xu, Siyu; Li, Jingchao; Zhang, Bin

doi:10.1098/rsos.191596

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…In this paper, the remaining 3124 knowledge data samples are used for test this scenario. And three common neural networks of GRNN, BP and RBF from another our previous work [36] are used as comparative machine learning models, the relative errors by these methods are shown in figures 11 to 15 and the root mean square errors by these methods are shown in Table 6. 6 and form figures 11 to 15, we can see that, compared with other machine learning models, the proposed deep learning model shows best diagnostic accuracy, and the overall root mean square error by the deep learning model does not exceed 0.033%, and the maximum relative error by the deep learning model does not exceed 0.36%, which illustrates the proposed method has great application potential.…”

Section: Sfmentioning

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: Sfmentioning

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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“…The introduction of the kernel function achieves the purpose of increasing the dimension, and the added parameters are adjustable and optimizable to prevent overfitting questions. Many scholars have indicated that the radial basis function net is an efficient tool for the fitting of a nonlinear relationship [25,33]. Therefore, in this study, a radial basis function was selected as the SVR kernel function to discover and interpret the nonlinear relationship between the stripping peak currents of Pb 2+ and Cu 2+ and the Pb 2+ concentration.…”

Section: Svr Modelingmentioning

confidence: 99%

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

Liu

Zhao

Liu

2020

Sensors

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In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu2+ contents on the SWASV signals of Pb2+ was investigated, and a nonlinear relationship between Pb2+ concentration and the peak currents of Pb2+ and Cu2+ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb2+ and Cu2+) and one output (i.e., Pb2+ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R2) and root-mean-square error (RMSE). Results showed that the SVR model with R2 and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb2+ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.

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“…A large number of ML methods for regression problems have been proposed and used in diverse engineering applications [6]- [8], [16], [17] to name some of them. Nevertheless, we focus on existing work that compares different approaches to regression problems for engineering applications.…”

Section: Related Workmentioning

confidence: 99%

A Comparison Framework of Machine Learning Algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction

2020

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Many engineering applications in the automotive, aeronautic, rubber, mechanics, and manufacturing industries collect multiple datasets measuring physical relations between input variables and performances for modeling purposes. The challenge relies on that such data is often highly dimensional, non-linear and contain mixed variables, i.e., numerical and categorical features, requiring specific algorithms and encoding schemes to perform regression task efficiently. Moreover, defining an appropriated similarity criterion for mixed-type data is a non-trivial task, especially when it is meant to be used in regression problems. This paper discusses the use of different machine learning algorithms for regression problems, involving mixed-type variables across multiple datasets. We use tire-related datasets as a case study to perform a rigorous, statistically founded comparison of different machine learning algorithms with encoding schemes to handle mixed variables in the prediction of tire-performances across multiple tirerelated datasets. Friedman's statistic and Nemenyi post-hoc tests are used to test the significance of performance differences between techniques and encoding strategies. Our contributions come as a series of recommendations for handling efficiently mixed-type variables while achieving high performances on regression tasks over multiple datasets. Furthermore, we provide a flexible and efficient similarity function between tires useful for tire comparison, prediction, and retrieval tasks. INDEX TERMS mixed-type variables, categorical encoding, Friedman Nemenyi, regression algorithms I. INTRODUCTION M ACHINE learning (ML) in engineering applications has grown in popularity during the last decades [1]-[5]. Many industrial applications use ML tools to build regression models for product design, performance optimization, variable design, fault detection, quality assessment, and others. For instance, the rubber industry, [6] uses non-linear least squares to estimate the tire-road friction coefficient for tire design. In automotive design, [7] employs support vector regression in structural optimization to vehicle crashworthiness design. More recently [8] performed thermodynamics compressor performance modeling for engine design with neural networks and non-linear support vector regression. A common characteristic of engineering data is its tabularlike structure, where rows represent data examples, which are themselves described as a mixture of numerical and categorical, i.e., mixed-type variables. For instance, in car crashworthiness design [9], vehicle structures must be designed to absorb crash energy through structural deformation as much as possible and attenuate the impact force to lower levels when impact occurs. The design variables are thickness related (measured in mm) and steel hardness types. For instance, the B-pillar inner, reinforce, the floor side and door beltline, are all numerical variables. However material design variables associated to the steel hardness, i.e., meal, medium, or high str...

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Compressor performance modelling method based on support vector machine nonlinear regression algorithm

Cited by 11 publications

References 27 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

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

A Comparison Framework of Machine Learning Algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction

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