Induction Motors Fault Diagnosis Using Machine Learning and Advanced Signal Processing Techniques PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Induction Motors Fault Diagnosis Using Machine Learning and Advanced Signal Processing Techniques PDF full book. Access full book title Induction Motors Fault Diagnosis Using Machine Learning and Advanced Signal Processing Techniques by Mohammad Zawad Ali. Download full books in PDF and EPUB format.
Author: Mohammad Zawad Ali
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In this thesis, induction motors fault diagnosis are investigated using machine learning and advanced signal processing techniques considering two scenarios: 1) induction motors are directly connected online; and 2) induction motors are fed by variable frequency drives (VFDs). The research is based on experimental data obtained in the lab. Various single- and multi- electrical and/or mechanical faults were applied to two identical induction motors in experiments. Stator currents and vibration signals of the two motors were measured simultaneously during experiments and were used in developing the fault diagnosis method. Signal processing techniques such as Matching Pursuit (MP) and Discrete Wavelet Transform (DWT) are chosen for feature extraction. Classification algorithms, including decision trees, support vector machine (SVM), K-nearest neighbors (KNN), and Ensemble algorithms are used in the study to evaluate the performance and suitability of different classifiers for induction motor fault diagnosis. Novel curve or surface fitting techniques are implemented to obtain features for conditions that have not been tested in experiments. The proposed fault diagnosis method can accurately detect single- or multi- electrical and mechanical faults in induction motors either directly online or fed by VFDs. In addition to the machine learning method, a threshold method using the stator current signal processed by DWT is also proposed in the thesis.
Author: Mohammad Zawad Ali
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In this thesis, induction motors fault diagnosis are investigated using machine learning and advanced signal processing techniques considering two scenarios: 1) induction motors are directly connected online; and 2) induction motors are fed by variable frequency drives (VFDs). The research is based on experimental data obtained in the lab. Various single- and multi- electrical and/or mechanical faults were applied to two identical induction motors in experiments. Stator currents and vibration signals of the two motors were measured simultaneously during experiments and were used in developing the fault diagnosis method. Signal processing techniques such as Matching Pursuit (MP) and Discrete Wavelet Transform (DWT) are chosen for feature extraction. Classification algorithms, including decision trees, support vector machine (SVM), K-nearest neighbors (KNN), and Ensemble algorithms are used in the study to evaluate the performance and suitability of different classifiers for induction motor fault diagnosis. Novel curve or surface fitting techniques are implemented to obtain features for conditions that have not been tested in experiments. The proposed fault diagnosis method can accurately detect single- or multi- electrical and mechanical faults in induction motors either directly online or fed by VFDs. In addition to the machine learning method, a threshold method using the stator current signal processed by DWT is also proposed in the thesis.
Author: Jawad Faiz
Publisher: IET
ISBN: 1785613286
Category : Business & Economics
Languages : en
Pages : 535
Get Book Here
Book Description
This book is a comprehensive, structural approach to fault diagnosis strategy. The different fault types, signal processing techniques, and loss characterisation are addressed in the book. This is essential reading for work with induction motors for transportation and energy.
Author: Mohamed Benbouzid
Publisher: IET
ISBN: 1785619578
Category : Technology & Engineering
Languages : en
Pages : 283
Get Book Here
Book Description
This book contains 5 chapters that discusses the following topics: Parametric signal processing approach; The signal demodulation techniques; Kullback-Leibler divergence for incipient fault diagnosis; Higher-order spectra and Fault detection and diagnosis based on principal component analysis.
Author: Subrata Karmakar
Publisher: Springer
ISBN: 9811006245
Category : Technology & Engineering
Languages : en
Pages : 182
Get Book Here
Book Description
This book covers the diagnosis and assessment of the various faults which can occur in a three phase induction motor, namely rotor broken-bar faults, rotor-mass unbalance faults, stator winding faults, single phasing faults and crawling. Following a brief introduction, the second chapter describes the construction and operation of an induction motor, then reviews the range of known motor faults, some existing techniques for fault analysis, and some useful signal processing techniques. It includes an extensive literature survey to establish the research trends in induction motor fault analysis. Chapters three to seven describe the assessment of each of the five primary fault types. In the third chapter the rotor broken-bar fault is discussed and then two methods of diagnosis are described; (i) diagnosis of the fault through Radar analysis of stator current Concordia and (ii) diagnosis through envelope analysis of motor startup current using Hilbert and Wavelet Transforms. In chapter four, rotor-mass unbalance faults are assessed, and diagnosis of both transient and steady state stator current has been analyzed using different techniques. If both rotor broken-bar and rotor-mass unbalance faults occur simultaneously then for identification an algorithm is provided in this chapter. Chapter five considers stator winding faults and five different analysis techniques, chapter six covers diagnosis of single phasing faults, and chapter seven describes crawling and its diagnosis. Finally, chapter eight focuses on fault assessment, and presents a summary of the book together with a discussion of prospects for future research on fault diagnosis.
Author: Nordin Saad
Publisher: CRC Press
ISBN: 1351172557
Category : Technology & Engineering
Languages : en
Pages : 150
Get Book Here
Book Description
The book covers various issues related to machinery condition monitoring, signal processing and conditioning, instrumentation and measurements, faults for induction motors failures, new trends in condition monitoring, and the fault identification process using motor currents electrical signature analysis. It aims to present a new non-invasive and non-intrusive condition monitoring system, which has the capability to detect various defects in induction motor at incipient stages within an arbitrary noise conditions. The performance of the developed system has been analyzed theoretically and experimentally under various loading conditions of the motor. Covers current and new approaches applied to fault diagnosis and condition monitoring. Integrates concepts and practical implementation of electrical signature analysis. Utilizes LabVIEW tool for condition monitoring problems. Incorporates real-world case studies. Paves way a technology potentially for prescriptive maintenance via IIoT.
Author: Irfan, Muhammad
Publisher: IGI Global
ISBN: 1522569901
Category : Technology & Engineering
Languages : en
Pages : 307
Get Book Here
Book Description
The reliability of induction motors is a major requirement in many industrial applications. It is especially important where an unexpected breakdown might result in the interruption of critical services such as military operations, transportation, aviation, and medical applications. Advanced Condition Monitoring and Fault Diagnosis of Electric Machines is a collection of innovative research on various issues related to machinery condition monitoring, signal processing and conditioning, instrumentation and measurements, and new trends in condition monitoring. It also pays special attention to the fault identification process. While highlighting topics including spectral analysis, electrical engineering, and bearing faults, this book is an ideal reference source for electrical engineers, mechanical engineers, researchers, and graduate-level students seeking current research on various methods of maintaining machinery.
Author: Shafi Md Kawsar Zaman
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions.
Author: Ruqiang Yan
Publisher: CRC Press
ISBN: 1040026613
Category : Computers
Languages : en
Pages : 272
Get Book Here
Book Description
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning.
Author:
Publisher:
ISBN: 9783725811694
Category : Technology & Engineering
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Sridevi R
Publisher: Self Publisher
ISBN: 9781835800782
Category : Computers
Languages : en
Pages : 0
Get Book Here
Book Description
"Induction Machine Performance Non-Invasive Assessment" by Sridevi R is a comprehensive and authoritative book that delves into the field of assessing and diagnosing the performance of induction machines through non-invasive techniques. This book provides a valuable resource for engineers, researchers, and practitioners seeking to enhance the reliability and efficiency of induction machines. The book covers a wide range of topics related to induction machine performance, including motor fault diagnosis, stator winding faults, rotor faults, bearing faults, eccentricity, broken rotor bars, inter-turn short circuits, and insulation degradation. It explores various diagnostic techniques such as vibration analysis, current signature analysis, voltage signature analysis, acoustic emission, temperature monitoring, thermal imaging, and electrical signature analysis. The author emphasizes both online and offline monitoring approaches for fault detection, classification, localization, and severity assessment. The book explores the use of advanced technologies such as machine learning, artificial intelligence, and data analytics for feature extraction, pattern recognition, and signal processing. It also presents diagnostic algorithms, fault indicators, and fault signatures for accurate fault identification, prognosis, and prediction. Throughout the book, the author focuses on electrical and mechanical faults, analyzing fault trends, patterns, and characteristics. The book provides insights into fault mitigation strategies, maintenance optimization, and performance optimization for induction machines. "Induction Machine Performance Non-Invasive Assessment" is a valuable reference for understanding and implementing effective non-invasive assessment techniques, ultimately enhancing the reliability, performance, and lifespan of induction machines in various industrial applications.
