| Titre : | Electric machines : modeling, condition monitoring, and fault diagnosis | | Type de document : | texte imprimé | | Auteurs : | Hamid A. Toliyat, Auteur ; Subhasis Nandi, Auteur ; Seungdeog Chol, Auteur | | Editeur : | New york : CRC Press/Taylor & Francis | | Année de publication : | 2013 | | Importance : | 259 p. | | Présentation : | couv. ill. en coul., ill. | | Format : | 24 cm. | | ISBN/ISSN/EAN : | 978-0-84937-027-4 | | Langues : | Anglais (eng) | | Catégories : | ELECTROTECHNIQUE
| | Index. décimale : | 10-03 Machines éléctriques | | Résumé : | With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme-thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect.
Written by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles.
Based on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.
| | Note de contenu : | Contents:
Chapter 1 : Introduction
Chapter 2 : Faults in DC, Induction and Synchronous motors
Chapter 3 : Modeling of Electric Machines using Winding and modified winding function approaches
Chapter 4 : Modeling of Electric Machines using Magnetic Equivalent Circuit Method
-inductance machines
Chapter 5 : analysis of faulty induction motors using Finite Element Method
Chapter 6 : Fault Diagnosis of Electric Machines using Techniques based on Frequency Domain
-detection of stator faults
-detection of rotor faults
-detection of eccentricity faults
Chapter 7 : Fault Diagnosis of Electric Machines using Model-Based Techniques
Chapter 8 : Fault Diagnosis of Electric Machines using Other Techniques
Chapter 9 : Application of Neural Network to Fault Diagnosis
Chapter 10 : Application of Wavelet to Fault Diagnosis
-(OBD) for hybrid electric vehicles(HEVs)
Chapter 11 : Application of Pattern Recognition to Fault Diagnosis
-decision-making sheme: adaptive threshold design (noise ambiguity compensation); Q function; noise estimation
-simulation and experimental result
-Index |
Electric machines : modeling, condition monitoring, and fault diagnosis [texte imprimé] / Hamid A. Toliyat, Auteur ; Subhasis Nandi, Auteur ; Seungdeog Chol, Auteur . - New york : CRC Press/Taylor & Francis, 2013 . - 259 p. : couv. ill. en coul., ill. ; 24 cm. ISBN : 978-0-84937-027-4 Langues : Anglais ( eng) | Catégories : | ELECTROTECHNIQUE
| | Index. décimale : | 10-03 Machines éléctriques | | Résumé : | With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme-thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect.
Written by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles.
Based on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.
| | Note de contenu : | Contents:
Chapter 1 : Introduction
Chapter 2 : Faults in DC, Induction and Synchronous motors
Chapter 3 : Modeling of Electric Machines using Winding and modified winding function approaches
Chapter 4 : Modeling of Electric Machines using Magnetic Equivalent Circuit Method
-inductance machines
Chapter 5 : analysis of faulty induction motors using Finite Element Method
Chapter 6 : Fault Diagnosis of Electric Machines using Techniques based on Frequency Domain
-detection of stator faults
-detection of rotor faults
-detection of eccentricity faults
Chapter 7 : Fault Diagnosis of Electric Machines using Model-Based Techniques
Chapter 8 : Fault Diagnosis of Electric Machines using Other Techniques
Chapter 9 : Application of Neural Network to Fault Diagnosis
Chapter 10 : Application of Wavelet to Fault Diagnosis
-(OBD) for hybrid electric vehicles(HEVs)
Chapter 11 : Application of Pattern Recognition to Fault Diagnosis
-decision-making sheme: adaptive threshold design (noise ambiguity compensation); Q function; noise estimation
-simulation and experimental result
-Index |
|  |
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