Survey of Insulation Systems in Electrical Machines

Insulating materials and insulation systems design have been gaining more attentions as more electrical machines tend to operate in harsher environments for various applications. Harsh environments include high temperature, humidity, erosion, low air pressure, etc. This paper discusses recent advances in insulation systems for electrical machines. Insulation tests as well as test standards that have been used to evaluate insulation systems and detect insulation failures will be discussed. Insulating materials used for a wide range of industrial applications such as wind turbine generators, aerospace hybrid/electric powertrain, and hydro generators have been summarized. For the emerging high-altitude, highvoltage aerospace applications, partial discharge and its impact on insulation systems will be discussed. Finally, polymer nanocomposite materials with excellent thermal conductivity and dielectric strength are highlighted as an outlook

Una revisión sobre detección y diagnostico de fallas en maquinas de inducción

In this work a careful review describing diferent types of failures in electricalmachines, their characteristic signals generated and diagnosis methodsis performed. Additionally a comparison of the advantages between theknown failure detection methods based on the information required for diagnosis, the occurrence and importance of failures detection, the effectiveness for anticipating a mal function or failure and the final diagnosis accuracy is also made. Particularly, this review will help to provide a straight forward update about the most recent work and research in this field. The work is mainly oriented to engineering students interested in starting the researchand study of electrical machines.El presente trabajo consiste en una revisión que describe los diferentes tipos de fallas, las formas características de las señales que generan y los métodos de diagnóstico en máquinas eléctricas. Además, se efectúa un comparativo de las ventajas de cada uno de los diferentes métodos de detección de fallas en función de la información que requieren para el diagnóstico, el número e importancia de las fallas que pueden detectar, la rapidez con la que son capaces de anticipar una falla y el grado de certeza en el diagnóstico final. En particular, esta revisión ayudará a proporcionar una visión rápida y clara acerca de los últimos trabajos y las nuevas investigaciones en el área. Esta enfocada principalmente hacia los estudiantes del área de ingeniería interesados en iniciarse en el campo de la investigación de máquinas eléctricas

Online monitoring of turn insulation deterioration in mains-fed induction machines using online surge testing

The development of an online method for the early detection of a stator turn insulation deterioration is the objective of the research at hand. A high percentage of motor breakdowns is related to the failure of the stator insulation system. Since most of the stator insulation failures originate in the breakdown of the turn-to-turn insulation, the research in this realm is of great significance. Despite the progress that has been made in the field of stator turn fault detection methods, the most popular and the best known ones are still limited to the detection of solid turn faults. The time span between a solid turn fault and the breakdown of the primary insulation system can be as short as a few seconds. Therefore, it is desirable to develop a method capable of detecting the deterioration of the turn insulation as early as possible and prior to the development of a solid turn fault. The different stresses that cause the aging of the insulation and eventually lead to failure are described as well as the various patterns of an insulation failure. A comprehensive literature survey shows the methods presently used for the monitoring of the turn insulation. Up to now no well-tested and reliable online method that can find the deterioration of the turn insulation is available. The most commonly used turn insulation test is the surge test, which, however, is performed only when the motor is out of service and disconnected from the supply. So far no research at all has been conducted on the application of an online surge test. The research at hand examines the applicability of the surge test to an operating machine. Various topologies of online surge testing are examined with regard to their practicability and their limitations. The most practical configuration is chosen for further analysis, implementation and development. Moreover, practical challenges are presented by the non-idealities of the induction machine like the eccentricity of the rotor and the rotor slotting, and have to be taken into account. Two solutions to eliminate the influence of the rotor position on the surge waveform are presented. Even though the basic concepts of online surge testing can be validated experimentally by a machine with a solid turn fault, it is preferable to use a machine with a deteriorated turn insulation. Therefore, a method, which does not require complex and expensive hardware, to experimentally emulate the turn insulation breakdown is implemented. The concepts at any stage of the work are supported by simulations and experimental results. In addition, the theory of surge testing is further developed by giving new definitions of the test's sensitivity, i.e., the frequency sensitivity and the error area ratio (EAR) sensitivity.Ph.D.Committee Chair: Thomas G. Habetler; Committee Member: Deepakraj M. Divan; Committee Member: J. Rhett Mayor; Committee Member: Linda S. Milor; Committee Member: Ronald G. Harle

State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors

Producción CientíficaDespite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies

Root cause analysis of the unpremeditated failure of induced draft fan motor during commissioning

Abstract: The objective of this research is to provide answers and identify the possible causes of the MV (medium voltage) motor failure. The research will assist the OEM(original equipment manufacturer) in reviewing their current processes used and the principal contractor’s site preservation team, to ensure that before operating non-operated equipment it will be checked and maintained in the storage period described by the OEM. The research will contribute to determining the causes of the thermal boiler 2 induction fan motor failures during the hot commissioning phase. The introduction of procedures and techniques to support principal contractor construction and commissioning to identify the potential challenges that will have an impact on the completion of the boilers, as this type of failure will delay the project by 18 weeks due to replacement material lead time. ..M.Ing. (Engineering Management

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.Riera-Guasp, M.; Antonino-Daviu, J.; Capolino, G. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 62(3):1746-1759. doi:10.1109/TIE.2014.2375853S1746175962

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi