Application of Artificial Neural Networks to Synchronous Generator Condition Monitoring PDF Download
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Author: Hongwei Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Hongwei Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jawad Faiz
Publisher: John Wiley & Sons
ISBN: 1119636078
Category : Technology & Engineering
Languages : en
Pages : 708
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Book Description
Electromagnetic Analysis and Condition Monitoring of Synchronous Generators Discover an insightful and complete overview of electromagnetic analysis and fault diagnosis in large synchronous generators In Electromagnetic Analysis and Condition Monitoring of Synchronous Generators, a team of distinguished engineers delivers a comprehensive review of the electromagnetic analysis and fault diagnosis of synchronous generators. Beginning with an introduction to several types of synchronous machine structures, the authors move on to the most common faults found in synchronous generators and their impacts on performance. The book includes coverage of different modeling tools, including the finite element method, winding function, and magnetic equivalent circuit, as well as various types of health monitoring systems focusing on the magnetic field, voltage, current, shaft flux, and vibration. Finally, Electromagnetic Analysis and Condition Monitoring of Synchronous Generators covers signal processing tools that can help identify hidden patterns caused by faults and machine learning tools enabling automated condition monitoring. The book also includes: A thorough introduction to condition monitoring in electric machines and its importance to synchronous generators Comprehensive explorations of the classification of synchronous generators, including armature arrangement, machine construction, and applications Practical discussions of different types of electrical and mechanical faults in synchronous generators, including short circuit faults, eccentricity faults, misalignment, core-related faults, and broken damper bar faults In-depth examinations of the modeling of healthy and faulty synchronous generators, including analytical and numerical methods Perfect for engineers working in electrical machine analysis, maintenance, and fault detection, Electromagnetic Analysis and Condition Monitoring of Synchronous Generators is also an indispensable resource for professors and students in electrical power engineering.
Author: Choon Meng Yin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Evangelos Tourvalis
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 99
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Book Description
In 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of vibratory and flight regime data obtained from sensors located throughout the aircraft. The purpose of this research is to predict failures of the UH-60L's electrical generators, applying Artificial Neural Networks (ANN) on the IMD-HUMS-produced data. Artificial NNs are data based vice rule based, thereby possessing the potential capability to operate where analytical solutions are inadequate. They are reputed to be robust and highly tolerant of noisy data. Software tools such as Clementine 10.0, S-Plus 7.0, and Excel are used to establish these predictions. This research has verified that ANNs have a position in machinery condition monitoring and diagnostics. However, the limited nature of these results indicates that ANNs will not solve all machinery condition monitoring and diagnostics problems by themselves. They certainly will not completely replace conventional rule-based expert systems. Ultimately, it is anticipated that a symbiotic combination of these two technologies will provide the optimal solution to the machinery condition monitoring and diagnostics problem.
Author: Qi Huang
Publisher: John Wiley & Sons
ISBN: 111949446X
Category : Technology & Engineering
Languages : en
Pages : 320
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Book Description
A comprehensive review of the development, challenges and utilisation of magnetic field measurement Magnetic Field Measurement with Applications to Modern Power Grids offers an authoritative review of the development of magnetic field measurement and the application of the technology to the smart grid. The authors, noted experts in the field, present the challenges to the field of magnetics and explore the use of cutting-edge magnetic technology in the development of the smart grid. In addition, the authors discussed the applications of magnetic field measurements in substations, generations systems, transmission systems and distribution systems. The specialized applications of magnetic field measurements in these venues are explored including the typical sensors used, the field strength levels and spectral frequencies involved and the mathematics that are needed to process data measurements. The book presents the complex topic of electromagnetics in clear and understandable terms. Magnetic Field Measurement with Applications to Modern Power Grids offers researchers in the magnetic community a guide to the progress of the smart grid and helps to inspire innovation of magnetic technologies in the smart grid. The technologies of measurement are a bridge between mathematical models and application oriented practice. The book is a guide to that bridge and: Offers a comprehensive review of the development of magnetic field measurement Shows how magnetic field measurement applies to the smart grid Outlines the challenges, trends and needs for future magnetic measurement systems Includes information on the need for levels of standardisation, smart grid applications and innovative sensors Written for researchers in smart grid, power engineers, power grid companies and professionals in the measurement and test industries, Magnetic Field Measurement with Applications to Modern Power Grids is an authoritative guide that offers a clear understanding of the relationship between the magnetic field measurement and power grids.
Author: Hosameldin Ahmed
Publisher: John Wiley & Sons
ISBN: 1119544629
Category : Technology & Engineering
Languages : en
Pages : 456
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Book Description
Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.
Author: Peter Vas
Publisher: Oxford University Press
ISBN: 9780198593973
Category : Computers
Languages : en
Pages : 794
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Book Description
Roughly half of all electricity generated is consumed in motors, and recent efforts to apply artificial intelligence (AI) to improving electric motors are receiving attention worldwide. At present two industrial drives incorporate some form of AI. This book is the first comprehensive discussion of AI applications to electrical machines and drives. It looks at d.c. drives, induction motor drives, synchronous motor drives, switched reluctance motor drives, and sensorless drives. It combines simple explanations of AI-based systems with detailed and unified mathematical and physical treatments, and it includes numerous worked examples, simulations, and experimental results.
Author: Seppo J. Ovaska
Publisher: Physica
ISBN: 379081783X
Category : Technology & Engineering
Languages : en
Pages : 363
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Book Description
This volume provides practicing engineers with new solutions to demanding real-world problems. It presents applications of soft computing to the field of industrial electronics in two categories, electric power applications and emerging applications.
Author: Paulo J. G. Lisboa
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 166
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Book Description
Author: Wei Zhang
Publisher: BoD – Books on Demand
ISBN: 9533070374
Category : Technology & Engineering
Languages : en
Pages : 514
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Book Description
In this book, a number of innovative fault diagnosis algorithms in recently years are introduced. These methods can detect failures of various types of system effectively, and with a relatively high significance.
