Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

Lü, Feng; Yafan, Wang; Huang, Jinquan; Huang, Yihuan

doi:10.3390/en81212403

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…There is a problem of basic PF algorithm that more particles have negligible weights after several iterations, and it indicates that particle generation degeneracy emerges and a large computational effort for updating particle becomes meaningless [18]. Then, importance re-sampling is added and each particle is assigned by the weight w i k = 1/N whenever the effective particles number N e f f is less than a threshold value N th…”

Section: Model-based Fault Diagnosis Using Pf-fl Methods In Feature Layermentioning

confidence: 99%

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

et al. 2016

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Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster-Shaffer evidence theory (WDS) is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM) is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL) by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

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Section: Model-based Fault Diagnosis Using Pf-fl Methods In Feature Layermentioning

confidence: 99%

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

et al. 2016

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“…GGTS provides various kinds of aircraft engine model, like turbojet, turbofan and turboshaft engine model and their simulation data, which has been well used in aviation industry corporation of China. The engine component characteristic maps and design operation data are loaded to GGTS to create a turbofan engine model (TEM) [14,37], which is coded using C language and packaged with a dynamic link library (DLL) for simulation in Matlab environment.…”

Section: Aircraft Engine Identification By Self-tuning Wiener Modelsmentioning

confidence: 99%

Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine

Lü

Huang

2017

Energies

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Abstract:In order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented Wiener models; the time constant values of linear dynamic element in Wiener model are designed to tune engine operating conditions. Reduced-dimension matrix inversion incorporated with the fast leave one out cross validation strategy is utilized to decrease computational time for the selection of engine model feature parameters. An optimization algorithm is no longer needed compared to the former method. The contribution of this study is that a more convenient and appropriate methodology is developed to describe aircraft engine thermodynamic behavior during its static and dynamic operations. The methodology is evaluated in terms of computational efforts, dynamic and static estimation accuracy through a case study involving data that are generated by general aircraft engine simulation. The results confirm our viewpoints in this paper.

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“…The average training time of fault cases by HMM, KPCA-HMM and IRKPCA-HMM are 26.75 s, 22.43 s and 17.75 s, respectively. The training time of the IRKPCA-HMM is the least among the examined algorithms.…”

Section: Of 21mentioning

confidence: 99%

“…LL* and Nc by three HMMs during dynamic process in the flight envelope at CN = 0. High-pressure spool speed T 22 Compressor inlet temperature P 22 Compressor inlet Pressure P 3 Compressor outlet pressure T 3 Compressor outlet temperature T 43 low pressure turbine inlet temperature P 43 low pressure turbine inlet pressure P 5 low pressure turbine outlet pressure T 6 mixing chamber inlet temperature W f Fuel flow A 8 Nozzle area…”

Section: Algorithms Reduced Features Hidden Statesmentioning

confidence: 99%

An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

et al. 2018

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To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The contribution of this paper is to develop an iterative reduced kernel principal component analysis (IRKPCA) algorithm to extract fault features from original high-dimension observation without large additional calculation load and combine it with the HMM for engine gas-path fault diagnosis. The optimal kernel features are obtained by iterative sequential forward selection of the IRKPCA, and the features with lower dimensions are contracted through a trade-off between the fault information and modeling data scale in reduced kernel space. The similarity degree is designed to simplify the HMM modeling data using fault kernel features. Test results show that the proposed methodology brings a significant improvement in diagnostic confidence and computational efforts in the applications of a turbofan engine fault diagnosis during its steady and dynamic process.

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Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

Cited by 8 publications

References 26 publications

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine

An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

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