Development of Reliable NARX Models of Gas Turbine Cold, Warm and Hot Start-Up

2017 ◽  
Author(s):  
Hilal Bahlawan ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
Pier Ruggero Spina ◽  
Mauro Venturini
Keyword(s):  
Gas Turbine ◽  
Training Data ◽  
Series Data ◽  
Data Sets ◽  
Control Logic ◽  
Start Up ◽  
Hot Start ◽  
Narx Models ◽  
Set Up ◽  
Rapid Transients

This paper documents the set-up and validation of nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine. The considered gas turbine is a General Electric PG 9351FA located in Italy. The data used for model training are time series data sets of several different maneuvers taken experimentally during the start-up procedure and refer to cold, warm and hot start-up. The trained NARX models are used to predict other experimental data sets and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training data sets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the set-up of a powerful, easy-to-build and very accurate simulation tool which can be used for both control logic tuning and gas turbine diagnostics, characterized by good generalization capability.

Development of Reliable NARX Models of Gas Turbine Cold, Warm, and Hot Start-Up

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Hilal Bahlawan ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
Pier Ruggero Spina ◽  
Mauro Venturini
Keyword(s):  
Gas Turbine ◽  
Generalization Capability ◽  
Control Logic ◽  
Performance Function ◽  
Start Up ◽  
Hot Start ◽  
Narx Models ◽  
Model Training ◽  
Rapid Transients ◽  
Nonlinear Autoregressive

This paper documents the setup and validation of nonlinear autoregressive network with exogenous inputs (NARX) models of a heavy-duty single-shaft gas turbine (GT). The data used for model training are time series datasets of several different maneuvers taken experimentally on a GT General Electric PG 9351FA during the start-up procedure and refer to cold, warm, and hot start-up. The trained NARX models are used to predict other experimental datasets, and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training datasets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the setup of a powerful, easy-to-build and very accurate simulation tool, which can be used for both control logic tuning and GT diagnostics, characterized by good generalization capability.

scholarly journals Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models

2014 ◽  
Author(s):  
Hamid Asgari ◽  
XiaoQi Chen ◽  
Raazesh Sainudiin ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Time Series Data ◽  
Model Development ◽  
Series Data ◽  
Data Sets ◽  
Heavy Duty ◽  
Start Up ◽  
Narx Models ◽  
Heavy Duty Gas Turbine ◽  
Nonlinear Autoregressive

In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation.

scholarly journals METHOD OF FORMULATING INPUT PARAMETERS OF NEURAL NETWORK FOR DIAGNOSING GAS-TURBINE ENGINES

Aviation ◽  
2013 ◽  
Vol 17 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Mykola Kulyk ◽  
Sergiy Dmitriev ◽  
Oleksandr Yakushenko ◽  
Oleksandr Popov
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Training Data ◽  
Turbine Engines ◽  
Data Sets ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Wide Range ◽  
And Training

A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.

scholarly journals Next generation reservoir computing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Daniel J. Gauthier ◽  
Erik Bollt ◽  
Aaron Griffith ◽  
Wendson A. S. Barbosa
Keyword(s):  
Time Series Data ◽  
Vector Autoregression ◽  
Learning Algorithm ◽  
Training Data ◽  
Series Data ◽  
Data Sets ◽  
Reservoir Computing ◽  
Next Generation ◽  
Training Time ◽  
Nonlinear Vector

AbstractReservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, fewer metaparameters, and provides interpretable results. Here, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time, heralding the next generation of reservoir computing.

scholarly journals Robust Federated Learning via Collaborative Machine Teaching

2020 ◽  
Vol 34 (04) ◽  
pp. 4075-4082
Author(s):  
Yufei Han ◽  
Xiangliang Zhang
Keyword(s):  
Teaching And Learning ◽  
Service Providers ◽  
Teaching Method ◽  
Training Data ◽  
Data Sets ◽  
Quality Of Data ◽  
Training Set ◽  
In The Wild ◽  
Model Training ◽  
Set Up

For federated learning systems deployed in the wild, data flaws hosted on local agents are widely witnessed. On one hand, given a large amount (e.g. over 60%) of training data are corrupted by systematic sensor noise and environmental perturbations, the performances of federated model training can be degraded significantly. On the other hand, it is prohibitively expensive for either clients or service providers to set up manual sanitary checks to verify the quality of data instances. In our study, we echo this challenge by proposing a collaborative and privacy-preserving machine teaching method. Specifically, we use a few trusted instances provided by teachers as benign examples in the teaching process. Our collaborative teaching approach seeks jointly the optimal tuning on the distributed training set, such that the model learned from the tuned training set predicts labels of the trusted items correctly. The proposed method couples the process of teaching and learning and thus produces directly a robust prediction model despite the extremely pervasive systematic data corruption. The experimental study on real benchmark data sets demonstrates the validity of our method.

Comparison of a Few Fault Diagnosis Methods on Sparse Variable Length Time Series Sequences

2007 ◽  
Author(s):  
Lokesh Kumar Sambasivan ◽  
Venkataramana Bantwal Kini ◽  
Srikanth Ryali ◽  
Joydeb Mukherjee ◽  
Dinkar Mylaraswamy
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Diagnosis ◽  
Variable Length ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Detection And Diagnosis ◽  
Time Sequences

Accurate gas turbine engine Fault Detection and Diagnosis (FDD) is essential to improving aircraft safety as well as in reducing airline costs associated with delays and cancellations. This paper compares broadly three methods of fault detection and diagnosis (FDD) dealing with variable length time sequences. Chosen methods are based on Dynamic Time Warping (DTW), k-Nearest Neighbor method, Hidden Markov Model (HMM) and a Support Vector Machine (SVM) which makes use of DTW ingeniously as its kernel. The time sequences are obtained from Turbo Propulsion Engines in their nominal conditions and two faulty conditions. Typically there is paucity of faulty exemplars and the challenge is to come up with algorithms which work reasonably well under such circumstances. Also, normalization of data plays a significant role in determining the performance of the classifiers used for FDD in terms of their detection rate and false positives. In particular spherical normalization has been explored considering the advantage of its superior normalization properties. Given sparse training data how well each of these algorithms performs is shown by means of tests performed on time series data collected at normal and faulty modes from a turbofan gas turbine propulsion engine and the results are presented.

Prediction of Dynamic Behavior of a Single Shaft Gas Turbine Using NARX Models

2021 ◽  
Author(s):  
Hamid Asgari ◽  
Emmanuel Ory
Keyword(s):  
Gas Turbine ◽  
Dynamic Behavior ◽  
Gas Turbines ◽  
Internal Combustion Engines ◽  
Closed Loop ◽  
Open Loop ◽  
Loop Model ◽  
Narx Models ◽  
Set Up ◽  
Matlab Programming

Abstract Gas turbines are internal combustion engines widely used in industry as main source of power for aircrafts, turbo-generators, turbo-pumps and turbo-compressors. Modelling these engines can help to improve their design and manufacturing processes, as well as to facilitate their operability and maintenance. These eventually lead to manufacturing of gas turbines with lower costs and higher efficiency at the same time. The models may also be employed to unfold nonlinear dynamics of these systems. The aim of this study is to predict the dynamic behavior of a single shaft gas turbine by using open-loop and closed-loop NARX models, which are subsets of artificial neural networks. To set up these models, datasets of significant variables of the gas turbine are used for training, test and validation processes. For this purpose, a comprehensive code is developed in MATLAB programming environment. In addition to the open-loop model, a closed-loop model is set up for multi-step prediction. The results of this study demonstrate the capability of the NARX models in reliable prediction of gas turbines’ dynamic behaviors over different operational ranges.

scholarly journals ARX Linear Model Set-Up for Fault Diagnosis of Gas Turbine Sensors

1997 ◽  
Author(s):  
R. Bettocchi ◽  
P. R. Spina
Keyword(s):  
Fault Diagnosis ◽  
System Identification ◽  
Gas Turbine ◽  
Linear Model ◽  
Time Series Data ◽  
System Modeling ◽  
Series Data ◽  
Model Parameters ◽  
Model Set ◽  
Set Up

The diagnosis of gas turbine sensor faults requires models of the system to calculate estimates of the measured output system variables. The model set-up phase is of great importance since the reliability of the diagnostic tool depends on the model accuracy. In the paper two different methodologies of I/O linear model set-up are analyzed and compared to find the more simple and general one. The first methodology consists in obtaining the I/O linear models by directly linearizing the physical laws (system modeling). The second one uses statistical methods (system identification) to calculate model parameters from time series data measured on the machine. The models used are of the ARX (Auto Regressive with eXternal input) type. The number of models and the measured variables correlated by each of them have been determined in order to obtain unambiguous fault signatures for each sensor. The system identification method proves to be more suitable to the system modeling because of its greater simplicity in the fault diagnosis application.

A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

2018 ◽  
Vol 35 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Bing Yu ◽  
Wenjun Shu ◽  
Can Cao
Keyword(s):  
Neural Networks ◽  
Time Series Data ◽  
Aircraft Engine ◽  
Modeling Method ◽  
Series Data ◽  
Data Sets ◽  
Wavelet Neural Networks ◽  
Component Level ◽  
Narx Models ◽  
Nonlinear Autoregressive

Abstract A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine’s dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Hamid Asgari ◽  
Mauro Venturini ◽  
XiaoQi Chen ◽  
Raazesh Sainudiin
Keyword(s):  
Power Plant ◽  
Modeling And Simulation ◽  
Gas Turbine ◽  
Transient Behavior ◽  
Normal Operation ◽  
Data Sets ◽  
Simulink Model ◽  
Model Training ◽  
Heavy Duty Gas Turbine ◽  
Set Up

This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation.

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