Artificial Neural Networks for Gas Turbine Monitoring PDF Download
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Author: Magnus Fast
Publisher:
ISBN: 9789174730357
Category :
Languages : en
Pages : 49
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Book Description
Author: Magnus Fast
Publisher:
ISBN: 9789174730357
Category :
Languages : en
Pages : 49
Get Book Here
Book Description
Author: Hamid Asgari
Publisher: CRC Press
ISBN: 1498777546
Category : Science
Languages : en
Pages : 216
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Book Description
Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consi
Author: Magnus Fast
Publisher:
ISBN:
Category :
Languages : en
Pages : 63
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Book Description
Author: Igor Loboda
Publisher:
ISBN:
Category : Computers
Languages : en
Pages :
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Book Description
The present chapter addresses the problems of gas turbine gas path diagnostics solved using artificial neural networks. As a very complex and expensive mechanical system, a gas turbine should be effectively monitored and diagnosed. Being universal and powerful approximation and classification techniques, neural networks have become widespread in gas turbine health monitoring over the past few years. Applications of such networks as a multilayer perceptron, radial basis network, probabilistic neural network, and support vector network were reported. However, there is a lack of manuals that summarize neural network applications for gas turbine diagnosis.
Author: Joao Luis Garcia Rosa
Publisher: BoD – Books on Demand
ISBN: 9535127047
Category : Computers
Languages : en
Pages : 416
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Book Description
The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.
Author: Hamid Asgari
Publisher: CRC Press
ISBN: 1498726631
Category : Science
Languages : en
Pages : 214
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Book Description
Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consi
Author: S-K. Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 124
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Book Description
Author: Gennady G. Kulikov
Publisher: Springer Science & Business Media
ISBN: 1447137965
Category : Technology & Engineering
Languages : en
Pages : 328
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Book Description
Whereas other books in this area stick to the theory, this book shows the reader how to apply the theory to real engines. It provides access to up-to-date perspectives in the use of a variety of modern advanced control techniques to gas turbine technology.
Author: Gurrappa Injeti
Publisher: BoD – Books on Demand
ISBN: 953307146X
Category : Technology & Engineering
Languages : en
Pages : 378
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Book Description
This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well.
Author: Ranjan Ganguli
Publisher: CRC Press
ISBN: 146650272X
Category : Science
Languages : en
Pages : 255
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Book Description
Widely used for power generation, gas turbine engines are susceptible to faults due to the harsh working environment. Most engine problems are preceded by a sharp change in measurement deviations compared to a baseline engine, but the trend data of these deviations over time are contaminated with noise and non-Gaussian outliers. Gas Turbine Diagnostics: Signal Processing and Fault Isolation presents signal processing algorithms to improve fault diagnosis in gas turbine engines, particularly jet engines. The algorithms focus on removing noise and outliers while keeping the key signal features that may indicate a fault. The book brings together recent methods in data filtering, trend shift detection, and fault isolation, including several novel approaches proposed by the author. Each method is demonstrated through numerical simulations that can be easily performed by the reader. Coverage includes: Filters for gas turbines with slow data availability Hybrid filters for engines equipped with faster data monitoring systems Nonlinear myriad filters for cases where monitoring of transient data can lead to better fault detection Innovative nonlinear filters for data cleaning developed using optimization methods An edge detector based on gradient and Laplacian calculations A process of automating fault isolation using a bank of Kalman filters, fuzzy logic systems, neural networks, and genetic fuzzy systems when an engine model is available An example of vibration-based diagnostics for turbine blades to complement the performance-based methods Using simple examples, the book describes new research tools to more effectively isolate faults in gas turbine engines. These algorithms may also be useful for condition and health monitoring in other systems where sharp changes in measurement data indicate the onset of a fault.
