Relevant bibliographies by topics / Broken rotor bar faults

Academic literature on the topic 'Broken rotor bar faults'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Broken rotor bar faults"

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Abdi Monfared, Omid, Aref Doroudi, and Amin Darvishi. "Diagnosis of rotor broken bars faults in squirrel cage induction motor using continuous wavelet transform." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 1 (January 7, 2019): 167–82. http://dx.doi.org/10.1108/compel-11-2017-0487.

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Purpose Squirrel cage induction motors suffer from several faults such as rotor broken bar. One of the powerful methods to detect induction motor faults is the line current signature analysis. This paper aims to present a novel algorithm based on continuous wavelet transform (CWT) to diagnose a rotor broken bar fault. Design/methodology/approach The proposed CWT has high flexibility in monitoring any frequency of interest in a waveform. Based on this transform, stator current frequency spectrum is analyzed to diagnose the rotor broken bar fault. The algorithm distinguishes the healthy motor from the faulted one based on a proper index. The method can be used in steady-state running time of induction motor and under different loading conditions. Experimental results are presented to show the validity of the proposed approach. Findings The proposed index considerably increases at the broken bars conditions compared to the healthy conditions. It can clearly diagnose the faulty conditions. The experimental results are found to be in good agreement with the theoretical and simulated results. The proposed method can reduce the noise and spectral leakage effects. Originality/value The main contribution of the paper are as follows: using CWT for detection of broken bar faults; introducing a proper index for diagnosing broken bars; and introducing a supplementary index to reduce the noise and spectral leakage effects.
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Valtierra-Rodriguez, Martin, Jesus R. Rivera-Guillen, Jesus A. Basurto-Hurtado, J. Jesus De-Santiago-Perez, David Granados-Lieberman, and Juan P. Amezquita-Sanchez. "Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors." Sensors 20, no. 13 (July 3, 2020): 3721. http://dx.doi.org/10.3390/s20133721.

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Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.
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Yin, Jintian, Yongfang Xie, Tao Peng, Chunhua Yang, and Zhiwen Chen. "Current Characteristics Analysis and Fault Injection of an Early Weak Fault in Broken Rotor Bar of Traction Motor." Mathematical Problems in Engineering 2018 (October 10, 2018): 1–8. http://dx.doi.org/10.1155/2018/4934720.

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Aiming at the destructive and irreversible problems of the broken rotor bar fault of the traction motor, the current characteristics of the early weak fault of the single bar are analyzed, and the broken rotor bar fault simulation injection is realized on the experimental platform. Firstly, a damage factor from the change rule of the metal resistance value of a rotor bar is defined. By means of such a damage factor, the relationship between the severity of the fracture of a single rotor bar and the phase resistance of the traction motor was obtained. Through the superposition principle, the traction motor in the fault of the rotor bar was regarded as a normal motor in which the reverse current source was superimposed on the fault rotor bar. The characteristic values of the stator current fault component were obtained when the single bar had broken. Finally, the relationship between the fault characteristics component of the stator current and the fracture severity of the single rotor bar was established. On this basis, on hardware-in-the-loop fault injection benchmark of the traction drive control system based on dSPACE, the gradual injection of early weak faults in the early broken rotor bar was carried out and the results were analyzed. The experimental data demonstrated the correctness of the current characteristics analysis and fault injection.
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Tang, Jing, Jie Chen, Kan Dong, Yongheng Yang, Haichen Lv, and Zhigang Liu. "Modeling and Evaluation of Stator and Rotor Faults for Induction Motors." Energies 13, no. 1 (December 26, 2019): 133. http://dx.doi.org/10.3390/en13010133.

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The modeling of stator and rotor faults is the basis of the development of online monitoring techniques. To obtain reliable stator and rotor fault models, this paper focuses on dynamic modeling of the stator and rotor faults in real-time, which adopts a multiple-coupled-circuit method by using a winding function approach for inductance calculation. Firstly, the model of the induction machine with a healthy cage is introduced, where a rotor mesh that consists of a few rotor loops and an end ring loop is considered. Then, the stator inter-turn fault model is presented by adding an extra branch with short circuit resistance on the fault part of a stator phase winding. The broken rotor bar fault is then detailed by merging and removing the broken-bar-related loops. Finally, the discrete models under healthy and faulty conditions are developed by using the Tustin transformation for digital implementation. Moreover, the stator and rotor mutual inductances are derived as a function of the rotor position according to the turn and winding functions distribution. Simulations and experiments are performed on a 2.2-kW/380-V/50-Hz three-phase and four-pole induction motor to show the performance of the stator and rotor faults, where the saturation effect is considered in simulations by exploiting the measurements of a no load test. The simulation results are in close agreement with the experimental results. Furthermore, magnitudes of the characteristic frequencies of 2f1 in torque and (1 ± 2s)f1 in current are analyzed to evaluate the stator and rotor fault severity. Both indicate that the stator fault severity is related to the short circuit resistance. Further, the number of shorted turns and the number of continuous broken bars determines the rotor fault severity.
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Chouidira, Ibrahim, Djalal Eddine Khodja, and Hani Benguesmia. "Detection and Diagnosis faults in Machine asynchronous based on single processing." International Journal of Energetica 4, no. 1 (June 30, 2019): 11. http://dx.doi.org/10.47238/ijeca.v4i1.89.

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In this work, we proposed multi-winding model for the simulation of broken bars in squirrel cage asynchronous machine, this model allows to study the influence of the broken bar defects on the behavior general of machines in different operating conditions (healthy and faulty). The breaking of the most frequent bars of the rotor causes oscillations of the torque, speed, and the current, the increase of the resistance of the rotor creates the defects proportional with the number of breaks bar K .The diagnosis fault using technique of single processing based on Spectrum analysis for detection broken bar. The results of the simulation obtained allowed us to show the importance of this technique for detection broken bar.
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Misra, Sajal, Satish Kumar, Sameer Sayyad, Arunkumar Bongale, Priya Jadhav, Ketan Kotecha, Ajith Abraham, and Lubna Abdelkareim Gabralla. "Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data." Sensors 22, no. 21 (October 26, 2022): 8210. http://dx.doi.org/10.3390/s22218210.

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The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.
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Liu, Xinyue, Yan Yan, Kaibo Hu, Shan Zhang, Hongjie Li, Zhen Zhang, and Tingna Shi. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition." Energies 15, no. 3 (February 7, 2022): 1196. http://dx.doi.org/10.3390/en15031196.

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When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.
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Ferrucho-Alvarez, Edna Rocio, Ana Laura Martinez-Herrera, Eduardo Cabal-Yepez, Carlos Rodriguez-Donate, Misael Lopez-Ramirez, and Ruth Ivonne Mata-Chavez. "Broken Rotor Bar Detection in Induction Motors through Contrast Estimation." Sensors 21, no. 22 (November 9, 2021): 7446. http://dx.doi.org/10.3390/s21227446.

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Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors’ knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state.
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Mesbeh, Amina, Marwen Jarboui, and Ahmed Masmoudi. "Broken bar and end-ring faults: analysis of their effects on the rotor cage currents." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 6 (November 2, 2015): 1771–95. http://dx.doi.org/10.1108/compel-06-2015-0222.

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Purpose – The purpose of this paper is to investigate the effects of different faulty scenarios on the induction motor (IM) operation with emphasis on the currents in the rotor bar and end rings. Design/methodology/approach – The modeling of the IM followed by a graphical representation-based analysis of the rotor steady-state currents under healthy operation is treated. Then, a case study is considered in order to investigate different faulty scenarios with a focus on the rotor cage currents. Findings – It has been found that the rotor faults greatly affect the currents in the bars and in the end rings both in amplitude and in harmonic content. These vary according to the relative positions with respect to the fault such that: the currents in the bars adjacent to the faulty one(s) have the highest amplitudes with the lowest harmonic content; and the ones in the ring portions adjacent to the faulty one have the lowest amplitudes with the highest harmonic content. Research limitations/implications – Although the simulated model has confirmed the well known IM behavior under healthy operation, it would be appreciated if the obtained results under faulty operation would be validated by finite element analysis. Practical implications – It is of great interest to investigate the effects of faulty scenarios on the rotor cage currents, in order to take appropriate actions starting from the design of the IM. Originality/value – A deep investigation (including the waveforms, the phasor diagrams and the harmonic content) of the effects of different faulty scenarios on the IM rotor cage currents represents the major contribution of this work.
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Xie, Ying, Ze Wang, Xueting Shan, and Yangyang Li. "Investigation of rotor thermal stress in squirrel cage induction motor with broken bar faults." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 5 (September 5, 2016): 1865–86. http://dx.doi.org/10.1108/compel-10-2015-0372.

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Purpose Thermal stress of the rotor in a squirrel cage induction motor is generated due to the temperature rise, and the structure of the rotor will be destroyed if the stress acted on the rotor exceeds its limits, so the thermal stress is also one of the main causes led to broken bar fault. The purpose of this paper is to report the thermal stress coupled analysis for the induction motor with healthy and faulty rotor, and to find the variation tendency of the temperature and thermal stress due to broken bars, and the part most likely to break in the rotor as a result of the thermal stress load are identified. Design/methodology/approach The steady temperature and thermal stress of the rotor in the case of the healthy and faulty conditions are calculated by finite element method, and the 3D model of the motor used in the experiments is established and the experimental results are presented for both healthy and faulty machines. Findings The influence of the broken bars fault on the motor thermal profile and thermal stress can be found, and it explains why the breaking point always appears in the joint of the bars and end rings. Originality/value The paper presents the 3D thermal stress coupled model and performance characteristics of induction motor with broken bars. The reasonable constraint is established according to the contact of components each other, and more reasonable fracture location is selected. The results obtained by the simulation model are in a good agreement with practical situation, because the effect of skewed rotor were taken into consideration in the process of simulation.
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Dissertations / Theses on the topic "Broken rotor bar faults"

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Dejan, Reljić. "Otkrivanje kvara rotora kaveznog asinhronog motora primenom tehnika analize terminalnih veličina." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104840&source=NDLTD&language=en.

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U disertaciji je predložena metoda za pouzdano otkrivanje kvara rotoratrofaznog kaveznog asinhronog motora. Metoda je zasnovana naidentifikaciji obeležja kvara rotora iz signala terminalnih veličinajednofazno napojenog trofaznog kaveznog motora u stacionarnom stanjupogona. Predložena metoda eliminiše potrebu za postojanja opterećenjamotora, što je osnovna prednost u odnosu na rešenja iz literature. Svateorijska razmatranja su praćena odgovarajućim rezultatima računarskihsimulacija, da bi se potom sprovela eksperimentalna verifikacija ipotvrdila efikasnost predložene metode. Takođe, predstavljen jepostupak za kvantitativnu procenu oštećenja štapnih provodnika rotora.
In this Doctoral dissertation, a novel method for broken rotor bar fault detectionin a three-phase squirrel-cage induction motor (IM) is introduced. The proposedmethod is non-invasive and it is based on the analysis of the IM terminalquantities in a single-phase steady-state operating condition. Compared toconventional fault detection techniques, the developed method does not requireany loading on the motor, which is the main benefit of the method. The faultycondition of broken rotor bars was investigated analytically, while theeffectiveness of the proposed method was proven by the variety of computersimulations and experimental results. Based on these results, a methodology forthe quantification of broken rotor bars in the IM has been presented.
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Ayhan, Bulent. "Linguistic Rule Generation for Broken Rotor Bar Detection in Squirrel-cage Induction Motors." NCSU, 2005. http://www.lib.ncsu.edu/theses/available/etd-12072005-232928/.

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In motor condition monitoring applications, traditional human expert approach for sensor exploitation is not cost-effective. The training requirements for human experts are extensive, and the overall training process is a very time-consuming task. In addition, the performance of human experts has limitations. For human experts, it is difficult to examine all the input-output data from the motor system under varying noise and motor load conditions. With a motor condition monitoring system that can automatically generate rules in the form of interpretable linguistic fuzzy "if-then" rules and membership functions, it would be easier for experts to understand and modify the rule base and also to track the motor condition for maintenance and replacement requirements. In this research, a methodology for fuzzy rule and membership function generation for broken rotor bar detection of squirrel-cage induction motors was developed. The methodology consists of a set of steps that an expert might do for fuzzy rule and membership function design. The methodology is named "H-ROC", since it utilizes histogram analysis with overlapping bins and a weighted cost function based on ROC (Receiver Operating Characteristics) curve analysis. As a second method, an existing fuzzy rule extraction method was extended to broken rotor bar detection problem. The performance and sensitivity analyses of the two methods were conducted.
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Dias, Cleber Gustavo. "Proposta de um novo método para a detecção de barras rompidas em motores de indução com rotor em gaiola." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-15092006-165225/.

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O estudo das condições de operação de um motor de indução em um ambiente industrial é indispensável, tendo em vista que eventuais problemas podem contribuir para um prejuízo na produção, ou ainda para custos adicionais relacionados à falta de manutenção dos equipamentos. Uma das principais falhas que podem ocorrer em um motor de indução do tipo gaiola de esquilo durante sua operação é o rompimento de uma ou mais barras que compõem o seu rotor. Apresenta-se neste trabalho um novo método para auxiliar na detecção de barras quebradas em um rotor tipo gaiola de esquilo, para um motor de grande porte, durante sua operação em regime permanente. A partir de um modelo matemático foi possível avaliar o rompimento de barras do rotor, detectando em uma posição específica, a variação da densidade de fluxo magnético resultante, produzida pela contribuição do fluxo de dispersão de cada barra do rotor, bem como pelo fluxo criado pelas correntes do estator. Um sensor de efeito Hall é instalado entre duas bobinas do estator, a fim de representar a posição onde é realizado o cálculo da densidade de fluxo magnético resultante pela modelagem matemática proposta. O sinal gerado pelo sensor a partir de uma falha é comparado com aquele obtido a partir do rotor saudável, para posterior análise. O trabalho sugere ainda a aplicação do método de detecção da falha em conjunto com uma técnica de inteligência artificial baseada nas redes neurais artificiais, a fim de contribuir para o diagnóstico da falha e estimativa do número de barras rompidas. Os resultados obtidos da simulação, bem como os dados obtidos durante o ensaio são apresentados e usados na validação do modelo matemático desenvolvido.
The study of operational conditions of an induction motor in an industrial environment is indispensable, once eventual problems can contribute for production losses, or still for additional costs related to the lack of equipments maintenance. Among the principal faults, in a squirrel cage induction motor can occur the breaking of one or more rotor bars. This work presents a new method in aid of detection of broken bars in a large squirrel cage induction motor during its operation in steady-state. A mathematical model is used to evaluate the broken rotor bars, detecting in a specific point, the resulting magnetic flux density produced by the leakage flux created by the rotor and stator currents. The Hall effect sensor is installed between two stator coils, in order to represent the position where the resulting magnetic flux density is calculated by the proposed mathematical model. The signal detected in the sensor during a fault, is compared to the obtained result of the magnetic flux density from a healthy rotor for analysis. The work still suggests the application of the artificial intelligence technique, based on artificial neural networks in the mathematical model, in order to aid on the fault detection and estimate of the number of broken bars. The simulation and experimental results are presented in order to validate the developed mathematical model.
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Duan, Fang. "Diagnostics of rotor and stator problems in industrial induction motors." Thesis, 2010. http://hdl.handle.net/2440/65202.

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In this project, two kinds of induction motor faults, stator short circuit fault and broken rotor bar fault, are investigated by using motor current signature analysis (MCSA) and zero crossing time (ZCT) method. These methods are based on the detection of sidebands around the supply frequency in the stator current signal. The thesis starts by a review of these two common faults and two commonly used diagnostic methods. Before the motor stator short circuit faults experiments, baseline analysis is carried out on two same types of healthy motors. Meanwhile, signal processing programs, composed in MATLAB and LABVIEW, are verified to ensure the accurate diagnosis of motor faults. Through a control box, artificial turn to turn fault and phase to phase fault are structured in each test. MCSA and ZCT are utilized to extract broken rotor bar information from recorded stator current signal. Although an induction motor is highly symmetrical, it may still have a detectable signal component at the fault frequencies due to imperfect manufacture, improper motor installation and so on. The misalignment experiments reveal that improper motor installation could lead to an unexpected frequency peak, which will affect motor fault diagnosis. Furthermore, manufacture tolerance and working environment could also result in disturbing the motor fault diagnosis. Through both online and offline experiments, MCSA and ZCT methods could detect particular abnormal harmonics related to stator short circuit fault and broken rotor bar fault. Compared with the conventional MCSA method, the ZCT method has the advantage of reduced computational burden.
Thesis (M.Eng.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2010
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Ahmed, Intesar. "Investigation of single and multiple faults under varying load conditions using multiple sensor types to improve condition monitoring of induction machines." 2008. http://hdl.handle.net/2440/58576.

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Condition monitoring involves taking measurements on an induction motor while it is operating in order to detect faults. For this purpose normally a single sensor type, for example current is used to detect broken rotor bar using fault frequency components only under the full-load condition or a limited number of load cases. The correlations among the different types of sensors and their ability to diagnose single and multiple faults over a wide range of loads have not been the focused in previous research. Furthermore, to detect different faults in machines using any fault frequency components, it is important to investigate the variability in its amplitude to other effects apart from fault severity and load. This area has also often been neglected in the literature on condition monitoring. The stator current and axial flux have been widely used as suitable sensors for detecting different faults i.e. broken rotor bar and eccentricity faults in motors. Apart from detecting the broken rotor bar faults in generalized form, the use of instantaneous power signal has often been neglected in the literature condition monitoring. This thesis aims to improve machine condition monitoring and includes accurate and reliable detection of single and multiple faults (faults in the presence of other faults) in induction machines over a wide range of loads of rated output by using current, flux and instantaneous power as the best diagnostic medium. The research presents the following specific tasks: A comprehensive real database from non–invasive sensor measurements, i.e. vibration measurements, axial flux, 3-phase voltage, 3-phase current and speed measurements of induction motor is obtained by using laboratory testing on a large set of identical motors with different single and multiple faults. Means for introducing these faults of varying severity have been developed for this study. The collected data from the studied machines has been analysed using a custom-written analysis programme to detect the severity of different faults in the machines. This helps to improve the accuracy and reliability in detecting of single and multiple faults in motors using fault frequency components from current, axial flux and instantaneous power spectra. This research emphasises the importance of instantaneous power as a medium of detecting different single and multiple faults in induction motor under varying load conditions. This enables the possibility of obtaining accurate and reliable diagnostic medium to detect different faults existing in machines, which is vital in providing a new direction for future studies into condition monitoring. Another feature of this report is to check the variability in healthy motors due to: test repeatability, difference between nominally identical motors, and differences between the phases of the same motor. This has been achieved by conducting extensive series of laboratory tests to examine fault frequency amplitudes versus fault severity, load, and other factors such as test repeatability and machine phases. The information about the variations in the amplitudes of the fault frequency components is used to check the accuracy and reliability of the experimental set-up, which is necessary for the practical application of the results to reliably detect the different faults in the machines reliably. Finally, this study also considers the detection of eccentricity faults using fault frequency amplitudes as a function of average eccentricity, instead of as a function of load under different levels of loading. This has not been reported in previous studies.
http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1298314
Thesis (Ph.D.)-- University of Adelaide, School of Electrical and Electronic Engineering, 2008
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Chen, Shuo. "Induction machine broken rotor bar diagnostics using prony analysis." 2008. http://hdl.handle.net/2440/49030.

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On-line induction machine condition monitoring techniques have been used widely in the detection of motor broken rotor bars for decades. Research has found that when broken bars occur in the machine rotor, the anomaly of electromagnetic field in the air gap will cause two sideband frequency components presenting in the stator current spectrum. Therefore, identification of these sideband frequencies can be used as a convenient and reliable approach to broken rotor bar fault diagnosis. Discrete Fourier Transform (DFT) is a conventional spectral analysis method used in this application. However, the use of DFT has several limitations. The most important one among them is the restriction of frequency resolution by window length. Due to this limitation, the accuracy of broken rotor bar detection can be highly affected in cases such as light machine load and limited data records. However, Prony's method for spectral analysis has the ability of overcoming the restriction of data window length on the frequency resolution, from which the DFT suffers. Such feature makes Prony's method a promising choice for broken rotor bar diagnosis when the machine is operating under light or varying load, or when only restricted data is available. In this thesis, I have demonstrated the implementation of this technique in the induction motor broken rotor bar detection, revealed its better performance than DFT in terms of maintaining high resolution in frequency domain whilst using a much shorter window, and analyzed the influential factors to the method of Prony Analysis (PA). In this thesis, an induction machine model that includes broken rotor bars is developed using Matlab/Simulink and verified by comparing the experimental and the simulated results. The Prony Analysis method for broken bar diagnosis is implemented and tested using both simulated and measured stator current data. Comparisons between PA and DFT results are presented, clearly indicating improvements of broken bar diagnostics using PA.
Thesis (M.Eng.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2008
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Al, Tuaimi Hamad A. "Detection of incipient rotor bar faults and air-gap asymmetries in squirrel-cage motors using stator current monitoring /." 2005. http://hdl.handle.net/1957/11805.

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Books on the topic "Broken rotor bar faults"

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Zagirnyak, Mykhaylo V., Zhanna Iv Romashykhina, and Andrii P. Kalinov. Diagnostics of Induction Motor Broken Rotor Bars on the Basis of the Electromotive Force Analysis. Nova Science Publishers, Incorporated, 2017.

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Book chapters on the topic "Broken rotor bar faults"

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Broken Rotor Bar." In Induction Motor Fault Diagnosis, 57–78. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_4.

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Tang, Jing, Chao Liang, Yuanhang Wang, Jie Chen, Qiang Huang, and Bin Shang. "Rotor Broken Bar Fault Diagnosis for Induction Traction Motor Considering Low Load Condition." In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021, 14–19. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9905-4_2.

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Verma, Alok, Pratul Arvind, Somnath Sarangi, Jayendra Kumar, and Anumeha. "Detection of Broken Rotor Bar Fault in an Induction Motor Employing Motor Current Signature Analysis." In Recent Advances in Power Electronics and Drives, 523–30. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9239-0_39.

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Li, Haiyang, Funso Otuyemi, Guojin Feng, Dong Zhen, Fengshou Gu, and Andrew Ball. "Application of Teager Energy for Broken Rotor bar Fault Detection Based on the Motor Current Signature Analysis." In Proceedings of IncoME-V & CEPE Net-2020, 523–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_49.

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Otuyemi, Funso, Haiyang Li, Fengshou Gu, and Andrew D. Ball. "Broken Rotor Bar Fault Detection Working at a Low Slip Using Harmonic Order Tracking Analysis Based on Motor Current Signature Analysis." In Proceedings of IncoME-V & CEPE Net-2020, 756–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_73.

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Arabaci, Hayri, and Osman Bilgin. "Efficiency Analysis of Submersible Induction Motor with Broken Rotor Bar." In Transactions on Engineering Technologies, 27–40. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9115-1_3.

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Kumar, Prashant, and Ananda Shankar Hati. "Support Vector Classifier-Based Broken Rotor Bar Detection in Squirrel Cage Induction Motor." In Lecture Notes in Mechanical Engineering, 429–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0550-5_42.

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Katalin, Ágoston. "Study and Simulation of a Broken Induction Motor Rotor Bar Caused Motor Vibration." In The 15th International Conference Interdisciplinarity in Engineering, 584–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93817-8_52.

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Wang, Zuolu, Haiyang Li, Dong Zhen, Fengshou Gu, and Andrew Ball. "Vibration Signature Analysis for Broken Rotor Bar Diagnosis in Induction Motors Based on Cyclic Modulation Spectrum." In Proceedings of IncoME-V & CEPE Net-2020, 616–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_59.

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Laribi, Souad Saadi, and Azzedine Bendiabdellah. "Induction Machine Rotor and Stator Faults Detection by Applying the N-F Network." In Advances in Computer and Electrical Engineering, 205–21. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6989-3.ch010.

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Abstract:
This chapter focuses on the monitoring and diagnosis of induction machine faults, particularly the broken rotor bars. The design of a system for monitoring, detecting, and locating incipient faults for different loads of the machine is achieved by the use of advanced intelligent techniques based on ANFIS-based neuro-fuzzy network. The knowledge base is based on indicators derived from the stator current spectral analysis of the machine which in addition has to detect and assess the number of faulty bars.
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Conference papers on the topic "Broken rotor bar faults"

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Edomwandekhoe, Kenneth, and Xiaodong Liang. "Current Spectral Analysis of Broken Rotor Bar Faults for Induction Motors." In 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). IEEE, 2018. http://dx.doi.org/10.1109/ccece.2018.8447776.

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Eddine Cherif, Bilal Djamal, Azeddine Bendiabdellah, and Sara Seninete. "Induction Motor Diagnosis with Broken Rotor Bar Faults Using DWT Technique." In 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2021. http://dx.doi.org/10.1109/icecce52056.2021.9514085.

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Edomwandekhoe, Kenneth, and Xiaodong Liang. "Advanced feature selection for broken rotor bar faults in induction motors." In 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS). IEEE, 2018. http://dx.doi.org/10.1109/icps.2018.8369981.

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Hamdani, S., O. Touhami, R. Ibtiouen, and M. Fadel. "Neural network technique for induction motor rotor faults classification-dynamic eccentricity and broken bar faults-." In 2011 8th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives - (SDEMPED 2011). IEEE, 2011. http://dx.doi.org/10.1109/demped.2011.6063689.

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Gyftakis, Konstantinos N., and Joya C. Kappatou. "Evaluation of the broken bar fault detectability depending on the rotor bar number." In IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2013. http://dx.doi.org/10.1109/iecon.2013.6699574.

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Arabaci, Hayri, and Osman Bilgin. "Diagnosis of broken rotor bar faults by using frequency spectrum of stator current envelope." In 2012 XXth International Conference on Electrical Machines (ICEM). IEEE, 2012. http://dx.doi.org/10.1109/icelmach.2012.6350100.

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Sharma, Amandeep, Lini Mathew, and Shantanu Chatterji. "Analysis of Broken Rotor bar Fault Diagnosis for Induction Motor." In 2017 International Conference on Innovations in Control, Communication and Information Systems (ICICCI). IEEE, 2017. http://dx.doi.org/10.1109/iciccis.2017.8660808.

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Fu, Jinliang, Chun Xiao, Yuanzheng Lu, and Yiqi Wang. "Study on Simulation of Asynchronous Motor Rotor Broken Bar Fault." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996853.

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Supangat, Randy, Jason Grieger, Nesimi Ertugrul, Wen L. Soong, Douglas A. Gray, and Colin Hansen. "Detection of Broken Rotor Bar Faults and Effects of Loading in Induction Motors during Rundown." In 2007 IEEE International Electric Machines & Drives Conference. IEEE, 2007. http://dx.doi.org/10.1109/iemdc.2007.383576.

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Khadim Moin Siddiqui and V. K. Giri. "Broken rotor bar fault detection in induction motors using Wavelet Transform." In 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET). IEEE, 2012. http://dx.doi.org/10.1109/icceet.2012.6203753.

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