A hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines

2015 ◽  
Author(s):  
Najmeh Daroogheh ◽  
Amir Baniamerian ◽  
Nader Meskin ◽  
Khashayar Khorasani
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Particle Filters ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Monitoring Strategy

Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks

2015 ◽  
Author(s):  
A. Vatani ◽  
K. Khorasani ◽  
N. Meskin
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Network Architecture ◽  
Information Criterion ◽  
Turbine Engines ◽  
Prediction Errors ◽  
Gas Turbine Engines ◽  
Dynamic Neural Networks ◽  
Turbine Engine

In this paper two artificially intelligent methodologies are proposed and developed for degradation prognosis and health monitoring of gas turbine engines. Our objective is to predict the degradation trends by studying their effects on the engine measurable parameters, such as the temperature, at critical points of the gas turbine engine. The first prognostic scheme is based on a recurrent neural network (RNN) architecture. This architecture enables ONE to learn the engine degradations from the available measurable data. The second prognostic scheme is based on a nonlinear auto-regressive with exogenous input (NARX) neural network architecture. It is shown that this network can be trained with fewer data points and the prediction errors are lower as compared to the RNN architecture. To manage prognostic and prediction uncertainties upper and lower threshold bounds are defined and obtained. Various scenarios and case studies are presented to illustrate and demonstrate the effectiveness of our proposed neural network-based prognostic approaches. To evaluate and compare the prediction results between our two proposed neural network schemes, a metric known as the normalized Akaike information criterion (NAIC) is utilized. A smaller NAIC shows a better, a more accurate and a more effective prediction outcome. The NAIC values are obtained for each case and the networks are compared relatively with one another.

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

2012 ◽  
Author(s):  
M. F. Abdul Ghafir ◽  
Y. G. Li ◽  
L. Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Gas Turbine ◽  
Life Prediction ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Creep Life ◽  
Model Based ◽  
Creep Life Prediction ◽  
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 and more complicated and therefore demand more computational time although they are more flexible in applications, in particular for new gas turbine engines. 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 back propagation neural networks have been utilised 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 generalisation 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%. Overall, it can be concluded that the proposed neural network approach in creep life prediction is able to provide a good alternative to the more complicated model-based creep life prediction algorithms and can be applied to different types of gas turbine engines.

Neural Networks for the Diagnostics of Gas Turbine Engines

1996 ◽  
Author(s):  
G. Torella ◽  
G. Lombardo
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Back Propagation ◽  
Learning Rate ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Classical Architecture ◽  
Set Up ◽  
The One

The paper describes the activities carried out for developing and testing Back Propagation Neural Networks (BPNN) for the gas turbine engine diagnostics. One of the aims of this study was to analyze the problems encountered during training using large number of patterns. Each pattern contains information about the engine thermodynamic behaviour when there is a fault in progress. Moreover the research studied different architectures of BPNN for testing their capability to recognize patterns even when information is noised. The results showed that it is possible to set-up and optimize suitable and robust Neural Networks useful for gas turbine diagnostics. The methods of Gas Path Analysis furnish the necessary data and information about engine behaviour. The best architecture, among the ones studied, is formed by 13, 26 and 47 neurons in the input, hidden and output layer respectively. The investigated Nets have shown that the best encoding of faults is the one using a unitary diagonal matrix. Moreover the calculation have identified suitable laws of learning rate factor (LRF) for improving the learning rate. Finally the authors used two different computers. The first one has a classical architecture (sequential, vectorial and parallel). The second one is the Neural Computer, SYNAPSE-1, developed by Siemens.

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