Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine

Barad, Sanjay; Ramaiah, P. Venkata; Giridhar, Rohith; Krishnaiah, G.

doi:10.1016/j.ymssp.2011.09.011

Cited by 64 publications

(34 citation statements)

References 5 publications

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“…Since the addition of the second hidden layer provides better results with lesser weights, it is beneficial where the input and output datasets are more, and the functions have a large peak and valley. Additionally, the supervised learning process is considered for those cases [26]. If the datasets are less ( < 100 ), it is recommended to use single hidden layer [4].…”

Section: Neural Network Training and Testingmentioning

confidence: 99%

Preliminary design, flow field, and thermal performance analysis of a helium turboexpander: a numerical approach

et al. 2019

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Most studies on cryogenic turboexpanders are focused on parametric studies and mean-line design to increase the performance of cryogenics liquefaction cycle without much attention to the splitter blades which are crucial for the stability of the flow field. This study focuses on a novel mean-line design methodology to develop the radial turbine and nozzle (hereafter renowned as turboexpander) to investigate the performance characteristics. Firstly, Sobol sensitivity analysis is performed to identify the effect of major non-dimensional design variables on isentropic efficiency of the turbine. Secondly, the non-dimensional design variables are optimized using artificial intelligence techniques. Thirdly, three turboexpander models with and without splitter blades are designed within the optimized range of non-dimensional variables. After that, the three-dimensional numerical analysis is carried out to visualize the effect of splitter blades on flow field and thermal characteristics of the turboexpander. It is noticed that the passage vortices and flow separation are minimized using the splitter blades. The numerical results are further validated with available data in the literature. A detailed comparative analysis of Mach number, pressure, temperature, velocity, static enthalpy, static entropy, etc., is carried out at different operating conditions. The results reveal that the use of splitter blades has a tremendous effect on the performance and flow field characteristics of the radial turbine. The proposed methodology specifies the insights for an optimum turbine design methodology of a cryogenic turboexpander, Sobol sensitivity analysis, prediction capability of artificial intelligence methods, numerical techniques to simulate the assimilating performance of turboexpander, as it is the most crucial and expensive component of turboexpander-based cryogenic system. KeywordsHelium turboexpander · Splitter blade · Sobol method · Flow pattern · Thermal characteristics · Artificial intelligence List of symbols Variables bBlade height (m) C Absolute velocity (m/s)

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Section: Neural Network Training and Testingmentioning

confidence: 99%

Preliminary design, flow field, and thermal performance analysis of a helium turboexpander: a numerical approach

et al. 2019

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“…Neural network (NN) is a well-known learning machine, and it is also an important data-based method applied to detect and isolate engine component and sensor faults [13]. Variants of the NNs are fulfilled with the goal of the smallest empirical risk, and the dimension disaster or over-fitting might occur due to a large number of samples required [22,23]. Support vector machine (SVM) is proposed on the basis of the minimum structural risk in the last twenty years, and this statistical learning approach has strictly mathematical deduction [24,25].…”

Section: Introductionmentioning

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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“…Gas turbines, with special focus on monitoring [38] and prediction [39] of performance, condition monitoring as an alternative to conventional scheduled maintenance [40], operation optimization of turbine compressor [41], health monitoring in military aircrafts engines [42] and turboshaft engines for helicopter propulsion [43], gas turbine diagnostics [44], fault isolation [45] with observation of model accuracy at partial load operation [46] and creep life prediction [47]. Boilers, with developed models aimed at predicting the performance of pulverized-coal boilers [48], predicting NO x emissions of CFB (circulating fluidised bed) boilers [49], estimating pollutant emissions in chain-grate stoker boilers [50], modelling NO x emissions in pulverized-coal boilers [51] with subsequent application of Genetic Algorithm for optimization purposes [52], predicting bottom ash depositing in pulverized-coal power plants [53] and reproducing the influence of fouling on the efficiency of biomass boilers [54].…”

Section: Introductionmentioning

confidence: 99%

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Rossi

Velázquez

2015

Energy

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Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine

Cited by 64 publications

References 5 publications

Preliminary design, flow field, and thermal performance analysis of a helium turboexpander: a numerical approach

Preliminary design, flow field, and thermal performance analysis of a helium turboexpander: a numerical approach

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

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

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