Faults In Electrical Machines Research Articles

Modeling and detecting the stator winding inter turn fault of permanent magnet synchronous motors using stator current signature analysis

Many researchers have been attracted by the challenges of electrical machines fault detection, diagnosis and monitoring, which provide early warning that could help schedule necessary maintenance to avoid catastrophic consequence. Considerable papers have presented reviews and compared conditions monitoring and fault diagnosis methods for induction machines, but none for permanent magnet machines. In this paper a dynamic fault model for a rotor surface mounted PMSM machine under inter-turn insulation failure is derived. this model allows to study the location and severity of the stator winding fault using electrical circuit magnetically coupled. To achieve this objective, a precise mathematical model that can describe both healthy and fault conditions is developed. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, power spectral density (PSD) was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM.

Investigation of Permanent Magnet Demagnetization in Synchronous Machines during Multiple Short-Circuit Fault Conditions

Faults in electrical machines can vary in severity and affect different parts of the machine. This study focuses on various kinds of short-circuits on the terminal side of a generic 20 kW surface mounted permanent magnet synchronous generator and how successive faults affect the performance of the machine. The study was conducted with the commercially available finite element method software COMSOL Multiphysics ® , and two time-dependent models for demagnetization of permanent magnets were compared, one using only internal models and the other using a proprietary external function. The study is simulation based and the two models were compared to a previously experimentally verified stationary model. Results showed that the power output decreased by more than 30% after five successive faults. In addition, the no-load voltage had become unsymmetrical, which was explained by the uneven demagnetization of the permanent magnets. The permanent magnet with the lowest reduction in average remanence was decreased by 0.8%, while the highest average reduction was 23.8% in another permanent magnet. The internal simulation model was about four times faster than the external model, but slightly overestimated the demagnetization.

Rotor Damping Effects in Dynamic Modeling of Three-Phase Synchronous Machines Under the Stator Interturn Faults—Winding Function Approach

Condition monitoring is used for early detection of incipient faults in electrical machines. Accurate modeling and simulation of the faulty machines can help to design effective condition monitoring systems. Three-phase synchronous machines (TSMs) are widely used for electrical power generation at power plants. Winding function theory offers effective, quick, and rather simple method for modeling and simulation of faulty electrical machines; however, this method only includes real windings of the machine. Rotor damping effects have important role in dynamic performance of the TSMs. Fictitious short-circuited windings (damper windings) on the rotor direct and quadrature axes are used to model these effects in the Park's vector model. This paper offers an approach to include similar damper windings in the winding function approach for modeling TSMs with interturn fault in the stator winding. Simulation results show creation or amplification of some frequency components in the stator line current as well as the rotor field current due to the interturn fault whose frequencies are equal to the rotor speed and its integer multiples. The frequency components are also present in the circulating fault current within the shorted turns. Experimental results approve the simulation results.

Incipient Fault Diagnosis in Ultrareliable Electrical Machines

Early detection of incipient faults in ac drives is one of the most difficult challenges for condition monitoring, especially for faults related to insulation degradation of the windings. In low power motors, the insulation degradation is principally due to the steep voltage variations caused by the voltage source converters that drive the machines. In the last years, with the coming to market of new no-Si based power devices, which achieve great values of dv/dt , fast tracking of electrical fault has became a topic of primary importance. In this paper, a new method to detect the presence of incipient faults in ultrareliable electric machines is presented and compared with a previous solution. The different methods were extensively evaluated by means of experimental results. It is shown that potential winding faults can be detected at an early stage of fault inception and thus measures can be taken to limit propagation.

Assessment of Time Frequency Color Index for Electrical Machines Diagnosis and Fault Severity

Improving availability and reliability of Induction Machine (IM) implies the minimization and the prediction of the maintenance operations. In this paper, we focus on detection and location of the rotor electrical faults in IM. This kind of fault generates non-stationary signatures in the stator current of the machine. Advanced signal processing techniques are required to deal with that type of signals. In this context, several recent studies suggest the use of Time-Frequency Representations (TFR) for the diagnosis of IMs' faults. This work presents a comparative study of some linear TFR. This last relies on good experimental results conducted on a test bench using a wound rotor induction machine operating in no load condition. The purpose is to showcase the TFR performances in faults detection. Static indicator is used to check the fault severity.

Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Early detection of faults in electrical machines, particularly in induction motors, has become necessary and critical in reducing costs by avoiding unexpected and unnecessary maintenance and outages in industrial applications. Additionally, most of these faults are due to problems in bearings. Thus, in this paper, experimental bearing fault detection of a three-phase induction motor is performed by analyzing the squared envelope spectrum of the stator current. Spectral kurtosis-based algorithms, namely, the fast kurtogram and the wavelet kurtogram, are also applied to improve the envelope analysis. Experimental tests are performed, considering outer bearing faults at different stages, and the results are promising.

Study on the Motor Vibration Based on LabVIEW and FFT

The detection of motor vibration can be used to evaluate the condition of motor. Furthermore the detection of motor vibration can be used to analyze the source of motor vibration and is helpful to reduce the motor vibration. Vibration is a cause of noise,so the vibration reduction is helpful to reduce the motor noise. Through analyzing the mechanism of motor vibration, a laptop computer and LabVIEW software are used to design the experimental system. the data acquisition system of vibration signal is composed of USB5935 data acquisition card, vibration acceleration sensor.LabVIEW is used to detect the vibration signal and record the vibration and realize the spectrum analysis. Through detecting and analyzing the vibration of the permanent magnet DC motor, It is proved that the motor vibration experimental system consisting of computer and LabVIEW can fully meet the requirements of electrical machine test and fault classification research.

La tendencia del factor de cresta ayuda a detectar eventos nacientes; circuito electrónico, programas y aplicaciones a señales de diversos campos

Within the signal analysis techniques in the time domain, the crest factor (CF) is undoubtedly one of the most simple and fast to implement using electronic circuits and/or software. That's why it has been used reliably to care for machinery and to evaluate the quality of supply. One of the major manufacturers of instruments for these purposes is Bruel and Kjaer and defines the crest factor of voltage or repetitive current signal as the ratio of the peak level and its rms value during a certain period of time. In this paper, we try to find out experimentally the potential of CF and their tendency to detect the nascent and evolution of events in various fields of knowledge, either by generating it with a developed electronic circuit, or with calculations, through routines that are performed with the programs DADISP and LabVIEW. The results are validated and checked for all the above factors and trends through a comparison between them and the proposed features and specifications. The results were acceptable so that the tools were applied to detect early faults in electrical machines, to identify chaosity differences between the circuits with these dynamics, to detect abnormal respiratory distress or rales in patients and to detect harmful distortions in the electrical current, all this based on simulations and measurements for each of the 4 cases studied. Other CF original applications proposed are: a) control of chaos in electronic circuits that stir/ mix industrial processes and b) correct the power factor of non-linear and inductive loads. A medium-term study and use a CF that considers the maximum signal peak to peak is contemplated, and it is thought that it can improve event detection.

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.

Application of Hilbert transform to fault detection in electric machines

In this study we investigate the possibility of broken bar fault detection in an induction machine rotor using spectral analysis of the stator currents. The numerical method, presented in this work, based on the Hilbert transform shows the possibility of improving the detection of faults in electrical machines. The results obtained using this method are validated experimentally on a test bench with an induction motor of 5.5 kW.

Sobre a aplicação de sistemas inteligentes para diagnóstico de falhas em máquinas de indução - uma visão geral

Muitas pesquisas estão sendo desenvolvidas buscando nos sistemas inteligentes soluções para diagnosticar falhas em máquinas elétricas. Estas falhas envolvem desde problemas elétricos, como curto-circuito numa das fases do estator, ate problemas mecânicos, como danos nos rolamentos. Dentre os sistemas inteligentes aplicados nesta área, destacam-se as redes neurais artificiais, os sistemas fuzzy, os algoritmos genéticos e os sistemas híbridos, como o neuro-fuzzy. Assim, o objetivo deste artigo é traçar um panorama geral sobre os trabalhos mais relevantes que se beneficiaram dos sistemas inteligentes nas diferentes etapas de análise e diagnóstico de falhas em motores elétricos, cuja principal contribuição está em disponibilizar diversos aspectos técnicos a fim de direcionar futuros trabalhos nesta área de aplicação.

Induction Drive Motor's Fault Diagnosis Research with Application

A number of induction machine is widely used in people's daily life and agriculture manufacture, and the chance to meet all kinds of breakdown is large. Electrical machine's breakdown not only damage electrical machine itself ，but also make electrical machine suddenly break down and product line breakdown, result in great economy's loss and fatal result. So, research on electrical machine's fault diagnosis' technology, Possess significant theory's meaning and society economic benefit. Texts according to induction drive motor's common breakdown's feature, Directing to the past research settle electrical machine's breakdown's means's fault , Bring up base on improve the smallest two ride stand by vector machine (LS- SVM) de induction drive motor's fault diagnosis' means. Adopt muddleheaded optimization algorithm, Seek come out possess compare strong popularize ability de parameter. Combine induction machine's structural parameter with performance export model de build mold example, with cross validation techniques to compare confirm research means be used for motor fault diagnosis de validity.

Turbinas Eólicas: Optimización en el Pre-Diagnóstico de Fallas en el Generador

A non-invasive low cost method for pre-diagnosis faults in electrical machines of large wind turbines is proposed. The objective is to minimize fault risks and the removal of the machine with heavy equipment (such as cranes) for maintenance. Since it is normal to use more than one method to detect incipient faults, prediction requires corroboration for higher security (such as vibration measurement and current spectrum analyses). This practice is usual in industries, but optimum predictive maintenance of electric machines in a wind turbine requires the use of the fewest possible devices and a distance non-invasive monitoring method. The proposed method has all this characteristics and preliminary essays with low cost sensors show good results for experimental applications.

Simulation of emerging faults in electric machines

The article presents simulation based on finite element technique of the most common faults in three-phase squirrelcage induction motors. Faults examined in this paper are: rotor broken bars and inter- inter turns short-circuit in stator windings. Fault in rotor broken bars was simulated taking two consecutive bars to see how the flux density is affected. Interturn short-circuit faults was simulated taking 40% inter-turn short-circuit of the windingin one phase. Simulations were performed using finite element software FEMM to obtain the flux density waveform in the airgap and in the electromagnetic core. Results from simulations were exported to MATLAB for further process and analysis. Flux density waveforms were plotted and processed by FFT analysis to observe harmonic distribution.

Detection of stator core faults in large electrical machines

Monitoring the health of large electrical machines, especially power station generators, is now an integral part of their operation to maintain and extend life. This work studies the use of electromagnetic sensors to detect inter-lamination insulation faults in the stator cores of large generators before they propagate to a level that can lead to catastrophic failure. The work develops a deeper understanding of the electromagnetic behaviour of core faults so that condition-monitoring sensors can be more specific about the location and severity of the fault. The study develops two new three-dimensional (3D) analytical models, one for predicting the fault current distribution in a stator core fault and the second for predicting the varying detection of such current by air-cored sensors. This further analysed the 3D detection efficiency of typical short fault lengths to compare with the two dimensional (2D) default of infinite-length faults. Different fault positions were modelled so that a clearer understanding of the location and severity of the fault is possible. These were validated on a specially constructed experimental test core that can impose controlled fault currents. The study also demonstrates how small core faults can escalate then self-limit radially, but may propagate axially into longer more damaging faults.

A Web-Based Remote Laboratory for Monitoring and Diagnosis of AC Electrical Machines

This paper deals with the development of a virtual platform for a Web-based remote application dedicated to condition monitoring and fault detection for ac electrical machines. The platform is based on several tools developed by using the LabVIEW software. Various techniques of condition monitoring and diagnosis of electrical and mechanical faults in ac electrical machines have been integrated such as the broken rotor bar, winding short circuit, bearing damage, or static/dynamic eccentricities. The main features are related to a user-friendly interface, a low-maintenance source code, and a standardized database for ac electrical machine diagnosis. The platform architecture, as well as three different test-rig configurations, has been described. The complete system can be controlled in both local and remote modes by using a simple Internet connection. Some remote experiences have been carried out between the University of Picardie “Jules Verne,” Amiens, France, and the University of Bologna, Bologna, Italy, to verify the effectiveness of the proposed system. The direct applications of this original package are based on diagnosis techniques applied to ac electrical machine faults. Some examples of rotor broken bar detection using classical techniques have been presented to show the effectiveness of the proposed platform. Further information will soon be available on the Open European Laboratory on Electrical Machines Web site: www.oelem.org.

Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection

A generalization of the particle swarm optimization (PSO) algorithm is presented in this paper. The novel optimizer, the Generalized PSO (GPSO), is inspired by linear control theory. It enables direct control over the key aspects of particle dynamics during the optimization process. A detailed theoretical and empirical analysis is presented, and parameter-tuning schemes are proposed. GPSO is compared to the classical PSO and genetic algorithm (GA) on a set of benchmark problems. The results clearly demonstrate the effectiveness of the proposed algorithm. Finally, an application of the GPSO algorithm to the fine-tuning of the support vector machines classifier for electrical machines fault detection is presented.

Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency components following a very characteristic evolution during the startup transient. The identification and extraction of these characteristic patterns through the Discrete Wavelet Transform (DWT) have been proven to be a reliable methodology for diagnosing the presence of these faults, showing certain advantages in comparison with the classical FFT analysis of the steady-state current. In the paper, a compilation of healthy and faulty cases are presented; they confirm the validity of the approach for the correct diagnosis of a wide range of electromechanical faults.

Permeance Network Modeling of the Stator Winding Faults in Electrical Machines

The aim of this paper is to present a robust model based on permeance network method (PNM) for the diagnosis of electrical machine stator faults. The proposed model allows taking into account the local magnetic saturation of the magnetic circuit due to heavy stator fault currents and remains moderately time consuming compared to the finite elements method. Firstly, the system of algebraic equations describing the developed permeance network is written. Then, this magnetic model is coupled to the electrical differential equations describing the induction machine in presence of inter-turn stator faults. Finally, simulation results are successfully compared to the experimental results. The impact of saturation on inter-turn fault signature clearly confirms the interest of a nonlinear model based diagnostic for this fault.

Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency components following a very characteristic evolution during the startup transient. The identification and extraction of these characteristic patterns through the Discrete Wavelet Transform (DWT) have been proven to be a reliable methodology for diagnosing the presence of these faults, showing certain advantages in comparison with the classical FFT analysis of the steady-state current. In the paper, a compilation of healthy and faulty cases are presented; they confirm the validity of the approach for the correct diagnosis of a wide range of electromechanical faults.