Exploitation of multi-models identification with decoupled states in twin shaft gas turbine variables for its diagnosis based on parity space approach

Aissat, Sidali; Hafaifa, Ahmed; Iratni, Abdelhamid; Guemana, Mouloud; Chen, Xiaoqi

doi:10.1007/s40435-021-00804-5

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…Fuzzy logic has been extensively applied in industrial applications, including system control, modeling and diagnosis, process monitoring, speech and image processing, data classification, decisionmaking, and more. Notable research studies in these areas include the works of Hafaifa et al [4], Ahmed Zohair Djeddi et al [5], Bezdek et al [11], Boon Chiang et al [12], Choayb Djeddi et al [13], Gustafson et al [17], Hakim Bagua et al [18], Jang et al [21], Merouane Alaoui et al [27][28], Nadji Hadroug et al [35][36] and Sidali Aissat et al [41][42]. The advantage of using fuzzy logic lies in its ability to incorporate human reasoning into analytical models.…”

Section: Nonlinear Modeling Systemmentioning

confidence: 99%

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

2023

ijSmartGrid

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This paper focuses on the identification and modeling of gas turbine dynamics, specifically those used in power generation plants. The approach utilizes experimental data and employs fuzzy reasoning systems. The resulting model serves the purpose of approximating nonlinear gas turbine systems and ensuring reliable system control. By incorporating uncertainties associated with human reasoning, such as fuzzy systems based on Takagi-Sugeno reasoning, it is possible to achieve highly reliable control systems. The primary goal of this paper is to increase the effective monitoring system by employing nonlinear identification techniques, namely fuzzy systems and neuro-fuzzy systems, based on real-time on-site experimental data. Additionally, the proposed identification approaches are evaluated through a comparative study, where the results obtained using the Nonlinear Autoregressive Exogenous Neural Networks (NARX-NN) modeling technique are compared with those obtained using the Adaptive Inference System combined with the techniques of Neuro-Fuzzy renowned ANFIS concept. The obtained investigation results further facilitate the comprehension and analysis of the nonlinearities present in these complex systems, ultimately aiding in the prediction of their dynamic behavior.

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Section: Nonlinear Modeling Systemmentioning

confidence: 99%

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

2023

ijSmartGrid

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“…Shoyama Tadayoshi [26] conducted a detailed analysis of bearing vibration phenomena, studying nonlinear bifurcations and ensuring overall stability. Sidali Aissat et al [27] employed a multi-model approach for the identification of turbine variables, using decoupled states to detect defects through residue generation based on parity space. Merouane Alaoui et al [14] proposed a turbine monitoring strategy using a generalized predictive adaptive control structure for turbine speed monitoring, which demonstrated a good response time and efficiency in regulating turbine parameters.…”

Section: Introductionmentioning

confidence: 99%

ARMAX-based identification and diagnosis of vibration behavior of gas turbine bearings

Mahroug,

Khaldi,

Guemana

et al. 2023

Diagnostyka

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Parametric identification approaches play a crucial role in the control and monitoring of industrial systems. They facilitate the identification of system variables and enable the prediction of their evolution based on the input-output relationship. In this study, we employ the ARMAX approach to accurately predict the dynamic vibratory behavior of MS5002B gas turbine bearings. By utilizing real input-output data obtained from their operation, this approach effectively captures the vibration characteristics of the bearings. Additionally, the ARMAX technique serves as a valuable diagnostic tool for the bearings, enhancing the quality of identification of turbine variables. This enables continuous monitoring of the bearings and real-time prediction of their behavior. Furthermore, the ARMAX approach facilitates the detection of all potential vibration patterns that may occur in the bearings, with monitoring thresholds established by the methodology. Consequently, this enhances the availability of the bearings and reduces turbine downtime. The efficacy of the proposed ARMAX approach is demonstrated through comprehensive results obtained in this study. Robustness tests are conducted, comparing the real behavior observed through various probes with the reference model, thereby validating the approach.

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Identification of twin-shaft gas turbine based on hybrid decoupled state multiple model approach

et al. 2023

Self Cite

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The work presented in this paper focuses on presenting an hybrid identification method for a nonlinear dynamic gas turbine, from a real time input and outputs data exploitation, with the fuel flow as the input and the rotational speed of high pressure and low pressure turbine as outputs. The multi model, which are in the form of a weighted combination of local linear state space models, offer an interesting alternative of the nonlinear models because it takes into account a several operating modes. The models are identified with the help of decoupled models using a hybrid approach between parametric estimation using artificial intelligence algorithms.

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Exploitation of multi-models identification with decoupled states in twin shaft gas turbine variables for its diagnosis based on parity space approach

Cited by 8 publications

References 47 publications

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

ARMAX-based identification and diagnosis of vibration behavior of gas turbine bearings

Identification of twin-shaft gas turbine based on hybrid decoupled state multiple model approach

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