A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research

Toward condition monitoring of damper windings in synchronous motors via EMD analysis

(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising 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] Failures in damper windings of synchronous machines operating in real facilities have been recently reported by several authors and companies. These windings are crucial elements of synchronous motors and generators, playing an important role in the asynchronous startup of these machines as well as in their stability during load transients. However, the diagnosis of failures in such elements has barely been studied in the literature. This paper presents a method to diagnose the condition of damper bars in synchronous motors. It is based on the capture of the stator current of the machine during a direct startup and its further analysis in order to track the characteristic transient evolution of a particular fault-related component in the time-frequency map. The fact that the damper only carries significant current during the startup and little or no current, when the machine operates in steady state, makes this transient-based approach specially suited for the detection of such failure. The Hilbert-Huang transform (based on the empirical mode decomposition method) is proposed as a signal-processing tool. Simulation and experimental results on laboratory synchronous machines prove the validity of the approach for condition monitoring of such windings. © 2012 IEEE.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) in the framework of the VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica 2008-2011. (Programa Nacional de proyectos de Investigacion Fundamental, project reference DPI2011-23740). Paper no. TEC-00443-2011.Antonino-Daviu, J.; Riera-Guasp, M.; Pons Llinares, J.; Roger-Folch, J.; Perez, R.; Charlton-Perez, C. (2012). Toward condition monitoring of damper windings in synchronous motors via EMD analysis. IEEE Transactions on Energy Conversion. 27(2):432-439. https://doi.org/10.1109/TEC.2012.2190292S43243927

Analysis of Ball Bearing Defects in Synchronous Machines using Electrical Measurements

Rolling element bearings are used in most electrical machines, especially for small and medium size applications. Under non-ideal operating conditions, ball bearing condition degrades by fatigue, ambient vibration, misalignment, overloading, contamination, corrosion from water or chemicals, improper lubrication, shaft currents and residual stress left from the bearing manufacturing process. All of these conditions eventually lead to increased vibration and acoustic noise during machine operation which at some point in time results in unexpected bearing failure. Over the years, a great number of publications have been devoted to the detection of mechanical faults, including rolling element bearing defects and torsional defects, in electrical machines based on Electrical Signature Analysis (ESA). It has been observed that these faults can affect either the stator to rotor air-gap distribution or the running speed of the machine, which can be reflected in the signature of the electrical signals. However, the physical link between the mechanical degradation and the electrical signature is still not explained well. A multi-physics model is developed by joining the detailed mechanical model of a rotor bearing system and the electrical model of a synchronous machine in this research. This combined model is capable of describing the transmission of information originating from bearing faults and their impact on the variations of the measured electrical signals. The electrical machine model is developed based on winding function approach and its validity is demonstrated by a more accurate Finite Element Method (FEM) model. The mechanical model consists of a high fidelity rotor-bearing system with detailed nonlinear ball bearing model and a flexible finite element shaft model. It is validated using the housing vibration data collected from some experiments. Generalized roughness bearing anomalies are linked to load torque ripples and airgap variations, while being related to current signature by phase and amplitude modulation. Considering that the induced characteristic signatures are usually subtle broadband changes in the current spectra, these signatures are easily affected by input power quality variations, machine manufacturing imperfections and environmental noise. In this research, a new algorithm is proposed to isolate the influence of the external disturbances of power quality, machine manufacturing imperfections and environmental noise, and to improve the effectiveness of applying the ESA for generalized roughness bearing defects. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous machines. Furthermore, the electrical signatures are analyzed in a synchronous machine with bearing defects. The proposed fault detection method employs a Zoomed Fast Fourier Transform (ZFFT) and Principal Component Analysis (PCA) and it is also tested on the available experimental data. The results show that amplitude induced electrical harmonics are related to the level of vibration, and the electrical signatures are affected heavily by other variables, such as power quality and load fluctuation. The proposed method is shown to be effective on detecting generalized roughness bearing defects in synchronous machines

Processing and inferential methods to improve shaft-voltage-based condition monitoring of synchronous generators

This thesis focuses on improving shaft-voltage-based condition monitoring of synchronous generators. The work presents theory for describing and modelling shaft voltages using fundamental electromagnetic principles. A modern framework is adopted in developing an online, automated and intelligent fault-diagnosis system. Novel processing and inferential methods are used by the system to provide accurate and reliable incipient-fault detection and diagnosis. The literature shows that shaft-voltage analysis is recognised as a technique with potential for use in condition monitoring. However, deficiencies in the fundamental theory and the inadequacy of methods for extracting useful information has limited its widespread application. This work extends the knowledge of shaft voltages, validates the merits of its use for fault diagnosis, and provides methods for practical application. Validation of the model is completed using an experimental synchronous generator, and results indicate that simulated shaft voltages compare well with the measurements - i.e. total average error of the model combined with experimental uncertainty is below 16%. The fault detection and diagnosis components are tested separately and together as a complete shaft-voltage-based conditionmonitoring system in an experimental setting. Results indicate that the system can accurately diagnose faults and it represents a unique and valuable contribution to shaft-voltage-based condition monitoring. Additionally, techniques such as optimal measurement selection, multivariate model monitoring, and fault inference developed for the investigations and system presented in this thesis, will assist engineers and researchers working in the field of condition monitoring of electrical rotating machines

Condition Monitoring System of Wind Turbine Generators

The development and implementation of the condition monitoring systems (CMS) play a significant role in overcoming the number of failures in the wind turbine generators that result from the harsh operation conditions, such as over temperature, particularly when turbines are deployed offshore. In order to increase the reliability of the wind energy industry, monitoring the operation conditions of wind generators is essential to detect the immediate faults rapidly and perform appropriate preventative maintenance. CMS helps to avoid failures, decrease the potential shutdowns while running, reduce the maintenance and operation costs and maintain wind turbines protected. The knowledge of wind turbine generators\u27 faults, such as stator and rotor inter-turn faults, is indispensable to perform the condition monitoring accurately, and assist with maintenance decision making. Many techniques are utilized to avoid the occurrence of failures in wind turbine generators. The majority of the previous techniques that are applied to monitor the wind generator conditions are based on electrical and mechanical concepts and theories. An advanced CMS can be implemented by using a variety of different techniques and methods to confirm the validity of the obtained electrical and mechanical condition monitoring algorithms. This thesis is focused on applying CMS on wind generators due to high temperature by contributing the statistical, thermal, mathematical, and reliability analyses, and mechanical concepts with the electrical methodology, instead of analyzing the electrical signal and frequencies trends only. The newly developed algorithms can be compared with previous condition monitoring methods, which use the electrical approach in order to establish their advantages and limitations. For example, the hazard reliability techniques of wind generators based on CMS are applied to develop a proper maintenance strategy, which aims to extend the system life-time and reduce the potential failures during operation due to high generator temperatures. In addition, the use of some advanced statistical techniques, such as regression models, is proposed to perform a CMS on wind generators. Further, the mechanical and thermal characteristics are employed to diagnose the faults that can occur in wind generators. The rate of change in the generator temperature with respect to the induced electrical torque; for instance is considered as an indicator to the occurrence of faults in the generators. The behavior of the driving torque of the rotating permanent magnet with respect to the permanent magnet temperature can also utilize to indicate the operation condition. The permanent magnet model describes the rotating permanent magnet condition during operation in the normal and abnormal situations. In this context, a set of partial differential equations is devolved for the characterization of the rotations of the permanent. Finally, heat transfer analysis and fluid mechanics methods are employed to develop a suitable CMS on the wind generators by analyzing the operation conditions of the generator\u27s heat exchanger. The proposed methods applied based on real data of different wind turbines, and the obtained results were very convincing

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

Studies in Electrical Machines & Wind Turbines associated with developing Reliable Power Generation

The publications listed in date order in this document are offered for the Degree of Doctor of Science in Durham University and have been selected from the author’s full publication list. The papers in this thesis constitute a continuum of original work in fundamental and applied electrical science, spanning 30 years, deployed on real industrial problems, making a significant contribution to conventional and renewable energy power generation. This is the basis of a claim of high distinction, constituting an original and substantial contribution to engineering science

Current based condition monitoring of electromechanical systems. Model-free drive system current monitoring: faults detection and diagnosis through statistical features extraction and support vector machines classification.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severityMinistry of Higher Education, Libya; Switchgear & Instruments Ltd