scholarly journals Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine

2004 ◽  
Vol 78 (4) ◽  
pp. 397-418 ◽  
Author(s):  
R.B. Joly ◽  
S.O.T. Ogaji ◽  
R. Singh ◽  
S.D. Probert
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Turbofan Engine ◽  
Artificial Neural ◽  
Bypass Ratio

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

2014 ◽  
Author(s):  
Dengji Zhou ◽  
Jiayun Wang ◽  
Huisheng Zhang ◽  
Shilie Weng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Small Sample ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Artificial Neural ◽  
Fault Diagnostic ◽  
Gas Path Fault ◽  
Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like non-linear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like artificial neural networks, need a large number of experimental data. Both are difficult to gain. Support vector machine (SVM), a novel computational learning method with excellent performance, seems to be a good choice for gas path fault diagnostic of gas turbine without engine model. In this paper, SVM is employed to diagnose a deteriorated gas turbine. And the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data, and can be employed to gas path fault diagnostic of gas turbine. Additionally, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnostic based on small sample.

Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks

2003 ◽  
Author(s):  
Pernilla Olausson ◽  
Daniel Ha¨ggsta˚hl ◽  
Jaime Arriagada ◽  
Erik Dahlquist ◽  
Mohsen Assadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Hybrid Model ◽  
Power Plants ◽  
Computational Time ◽  
Process Identification ◽  
Calculation Time ◽  
Artificial Neural

Traditionally, when process identification, monitoring and diagnostics are carried out for power plants and engines, physical modeling such as heat and mass balances, gas path analysis, etc. is utilized to keep track of the process. This type of modeling both requires and provides considerable knowledge of the process. However, if high accuracy of the model is required, this is achieved at the expense of computational time. By introducing statistical methods such as Artificial Neural Networks (ANNs), the accuracy of the complex model can be maintained while the calculation time is often reduced significantly reduced. The ANN method has proven to be a fast and reliable tool for process identification, but the step from the traditional physical model to a pure ANN model is perhaps too wide and, in some cases, perhaps unnecessary also. In this work, the Evaporative Gas Turbine (EvGT) plant was modeled using both physical relationships and ANNs, to end up with a hybrid model. The type of architecture used for the ANNs was the feed-forward, multi-layer neural network. The main objective of this study was to evaluate the viability, the benefits and the drawbacks of this hybrid model compared to the traditional approach. The results of the case study have clearly shown that the hybrid model is preferable. Both the traditional and the hybrid models have been verified using measured data from an existing pilot plant. The case study also shows the simplicity of integrating an ANN into conventional heat and mass balance software, already implemented in many control systems for power plants. The access to a reliable and faster hybrid model will ultimately give more reliable operation, and ultimately the lifetime profitability of the plant will be increased. It is also worth mentioning that for diagnostic purposes, where advanced modeling is important, the hybrid model with calculation time well below one second could be used to advantage in model predictive control (MPC).

Gas turbine gas-path fault identification using nested artificial neural networks

2018 ◽  
Vol 90 (6) ◽  
pp. 992-999 ◽  
Author(s):  
Amare D. Fentaye ◽  
Aklilu T. Baheta ◽  
Syed Ihtsham Ul-Haq Gilani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Diagnostic Technique ◽  
Identification Accuracy ◽  
Fault Identification ◽  
Content Type ◽  
Artificial Neural ◽  
Fault Diagnostic

Purpose The purpose of this paper is to present a quantitative fault diagnostic technique for a two-shaft gas turbine engine applications. Design/methodology/approach Nested artificial neural networks (NANNs) were used to estimate the progressive deterioration of single and multiple gas-path components in terms of mass flow rate and isentropic efficiency indices. The data required to train and test this method are attained from a thermodynamic model of the engine under steady-state conditions. To evaluate the tolerance of the method against measurement uncertainties, Gaussian noise values were considered. Findings The test results revealed that this proposed method is capable of quantifying single, double and triple component faults with a sufficiently high degree of accuracy. Moreover, the authors confirmed that NANNs have derivable advantages over the single structure-based methods available in the public domain, particularly over those designed to perform single and multiple faults together. Practical implications This method can be used to assess engine’s health status to schedule its maintenance. Originality/value For complicated gas turbine diagnostic problems, the conventional single artificial neural network (ANN) structure-based fault diagnostic technique may not be enough to get robust and accurate results. The diagnostic task can rather be better done if it is divided and shared with multiple neural network structures. The authors thus used seven decentralized ANN structures to assess seven different component fault scenarios, which enhances the fault identification accuracy significantly.

Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks

2013 ◽  
Vol 136 (3) ◽  
Author(s):  
M. F. Abdul Ghafir ◽  
Y. G. Li ◽  
L. Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Life Prediction ◽  
Creep Life ◽  
Model Based ◽  
Creep Life Prediction ◽  
Artificial Neural ◽  
Prediction Approach

Accurate and reliable component life prediction is crucial to ensure safety and economics of gas turbine operations. In pursuit of such improved accuracy and reliability, model-based creep life prediction methods have become more complicated and demand higher computational time. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to creep life prediction for production engines while at the same time maintain the same accuracy and reliability as that of the model-based methods. In this paper, a novel creep life prediction approach using artificial neural networks is introduced as an alternative to the model-based creep life prediction approach to provide a quick and accurate estimation of gas turbine creep life. Multilayer feed forward backpropagation neural networks have been utilized to form three neural network–based creep life prediction architectures known as the range-based, functional-based, and sensor-based architectures. The new neural network creep life prediction approach has been tested with a model single-spool turboshaft gas turbine engine. The results show that good generalization can be achieved in all three neural network architectures. It was also found that the sensor-based architecture is better than the other two in terms of accuracy, with 98% of the post-test samples possessing prediction errors within ±0.4%.

Complex Identification Technique of Aircraft Gas Turbine Engine’s Health

2003 ◽  
Author(s):  
A. M. Pashayev ◽  
R. A. Sadiqov ◽  
P. S. Abdullayev
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Soft Computing ◽  
Technical Condition ◽  
New Approach ◽  
Identification Technique ◽  
Artificial Neural

The new approach to identification of the aviation GTE technical condition is considered (examined) at an fuzzy, limitation and uncertainty of the information. This approach is based on applicability of fuzzy logic and artificial neural networks (Soft computing).

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