Two-shaft stationary gas turbine engine gas path diagnostics using fuzzy logic

Amare, F. D.; Gilani, Syed I.U.; Aklilu, B. T.; Mojahid, A.

doi:10.1007/s12206-017-1053-9

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…A commonly used method is to divide the domain of the universe of discourse (UOD) into several parts, then the centers of each part are chosen as the MFs' centers. [29] and [30] applied this method to gas turbine FDI system design. Another kind of method is data-based, in which the centers are determined by clustering or expert experience according to the data.…”

Section: Membership Functions Constructionmentioning

confidence: 99%

Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties

Shuai

Zheng

et al. 2022

Preprint

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In this paper, a novel performance-based fault detection and identification (FDI) strategy for turbofan gas turbine engines is proposed based on a first-order Takagi-Sugeno-Kang (TSK) fuzzy inference system. To deal with the problem of ambient condition changes, we use parameter correction to preprocess the raw measurement data, which can reduce the complexity of the FDI system. Also, the power level angle is set to be a scheduling parameter to reduce the rule number of the TSK-based FDI system. The data used to design, train, and test for the proposed FDI strategy are generated using a component-level turbofan engine model. The antecedent and consequent parameters of the proposed TSK-based FDI system are optimized using particle swarm optimization algorithm and ridge regression method. Then a robust structure against measurement biases is proposed by combining a special fuzzy inference system with the TSK-based FDI system. The performances of the first-order TSK-based FDI system and the robust FDI structure are evaluated through comprehensive simulation studies. The comparative studies confirm the superiority of the first-order TSK-based FDI system in terms of accuracy on fault detection, isolation, and identification. The robust structure has a 2%-8% improvement under relatively large measurement bias conditions in terms of successful rate index, which is demonstrated to have excellent robustness against measurement biases. Accuracy against a large scale of bias values and the computation time have been shown through comprehensive case simulations, which indicates that our proposed robust structure has desirable online performance.

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Section: Membership Functions Constructionmentioning

confidence: 99%

Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties

Shuai

Zheng

et al. 2022

Preprint

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“…The gas (3) enters the high-pressure turbine to expand to do work, the generated energy (8) drives the high-pressure compressor to rotate, and the expanded gas (4) enters the low-pressure turbine (LC) for further expansion, the energy (9) generated by low-pressure turbine drives the low-pressure compressor to rotate. Finally, the expanded gas (5) enters the power turbine (PT) to continue to expand to do work, the generated energy (6) drives the generator(G) to generate electricity (7), and the exhaust (11) is discharged into the atmosphere. According to the energy flow and mass flow described in Figure 2, the physical structure of the gas turbine is shown in Figure 3.…”

Section: Eil Analysis Of Gas Turbinementioning

confidence: 99%

“…In recent years, many gas path fault diagnosis methods have been proposed to assess gas turbine health status, such as nonlinear gas path analysis [3][4][5], rule-based fuzzy expert system [6,7], Bayesian hierarchical models [8,9], neural networks [10][11][12], genetic algorithm [13,14], multiple-model method [15,16], and exergy analysis [17]. Gas path analysis is an inversely mathematical problem to obtain the deviation of component performance parameters over gas path measured variables.…”

Section: Introductionmentioning

confidence: 99%

A Marine Gas Turbine Fault Diagnosis Method Based on Endogenous Irreversible Loss

et al. 2019

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When a malfunction occurs in a marine gas turbine, its thermal efficiency will decrease slightly, and the gas path fault is often difficult to distinguish. In order to solve this problem, based on the second law of thermodynamics, the endogenous irreversible loss (EIL) model of the marine gas turbine is established, and the exergy loss analysis under normal conditions is carried out to verify the accuracy of the model. The fault diagnosis of gas turbine gas path based on EIL is proposed, and a simulation experiment conducted on a three-shaft marine gas turbine demonstrated that the proposed approach can detect and isolate gas path fault accurately under different operating conditons and enviroments.

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“…A marine gas turbine operates under hostile ocean environments. The air contains salt-ingested particles, which will have an impact on the gas-path components such as the compressor, combustion chamber, and turbine, and can lead to fouling, erosion, and corrosion [1,2]. These faults will change the structure of the components and cause performance degradation, reducing the safety and stability of the gas turbine [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Such relationships were described by Urban who proposes the linear gas-path analysis method [8]. In recent years, many gas-path fault diagnosis methods have been proposed to assess gas turbine health status, such as non-linear gas-path analysis [9][10][11], the rule-based fuzzy expert system [2,12,13], Bayesian hierarchical models [14,15], neural networks [16][17][18][19], the genetic algorithm [4,20,21], the multiple-model method [3,22] and exergy analysis [23]. Gas-path analysis is an inversely mathematical problem to obtain the deviation of component performance parameters over gas-path measured variables.…”

Section: Introductionmentioning

confidence: 99%

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Cao

Han

et al. 2019

Energies

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In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

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Two-shaft stationary gas turbine engine gas path diagnostics using fuzzy logic

Cited by 16 publications

References 34 publications

Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties

Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties

A Marine Gas Turbine Fault Diagnosis Method Based on Endogenous Irreversible Loss

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

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