Relevant bibliographies by topics / Broken rotor bar

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Broken rotor bar'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Broken rotor bar"

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Khan, Rizwanullah, Mohd Fairouz Mohd Yousof, Rahisham Abd Rahman, Norhafiz Azis, Salem Al-Ameri, and Asjad Ali. "Broken rotor bar detection of three phase induction motor using frequency response analysis." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1289. https://doi.org/10.11591/ijece.v15i2.pp1289-1296.

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Three phase induction motors (TPIMs) are broadly utilized for various applications in the industry, but they are prone to different faults that can affect their performance and reliability. One common fault is a broken rotor bar, which leads to vibration, noise, and reduced efficiency. Therefore, detecting and identifying this fault early is important to avoid further damage and reduce maintenance costs. This paper proposes a novel method using frequency response analysis (FRA) to diagnose broken rotor bars in a TPIM. The response of normal motor is measured to obtain the baseline. Subsequently, the rotor was inflected with physical damage to represent a broken rotor bar. By comparing normal and faulty rotors, the measurement shows that frequency response analysis is sensitive toward various fault severity based on the number of broken rotor bars.
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Khan, Rizwanullah, Mohd Fairouz Mohd Yousof, Rahisham Abd Rahman, Norhafiz Azis, Salem Al-Ameri, and Asjad Ali. "Broken rotor bar detection of three phase induction motor using frequency response analysis." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1289–96. https://doi.org/10.11591/ijece.v15i2.pp1289-1296.

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Three phase induction motors (TPIMs) are broadly utilized for various applications in the industry, but they are prone to different faults that can affect their performance and reliability. One common fault is a broken rotor bar, which leads to vibration, noise, and reduced efficiency. Therefore, detecting and identifying this fault early is important to avoid further damage and reduce maintenance costs. This paper proposes a novel method using frequency response analysis (FRA) to diagnose broken rotor bars in a TPIM. The response of normal motor is measured to obtain the baseline. Subsequently, the rotor was inflected with physical damage to represent a broken rotor bar. By comparing normal and faulty rotors, the measurement shows that frequency response analysis is sensitive toward various fault severity based on the number of broken rotor bars.
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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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Sun, Li Ling, and Kai Bin Chen. "Broken Rotor Bar Fault Detection Analysis." Advanced Materials Research 383-390 (November 2011): 1862–66. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1862.

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Induction motor is widely applied to people's lives and production. This paper presents the simple and sophisticated of some methods which are used to diagnose the rotor fault. After analyzing the advantage and disadvantage of these methods, this paper tells what the key of rotor fault diagnosis of induction motor is, and put forward a new method.
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Goktas, Taner, and Müslüm Arkan. "Discerning broken rotor bar failure from low-frequency load torque oscillation in DTC induction motor drives." Transactions of the Institute of Measurement and Control 40, no. 1 (2016): 279–86. http://dx.doi.org/10.1177/0142331216654964.

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This paper proposes a method for separation of broken rotor bar failures from low-frequency load torque oscillation in direct torque control (DTC) induction motor drives by using vq voltage and iq current components’ spectra. The effect of load torque oscillation should be considered in induction motor drives for reliable broken bar fault detection. Induction machine drivers are run in DTC mode to control its torque and speed. In practice, the presence of load torque fluctuation may sometimes cause false positive alarms on stator current spectrum. However, discerning of broken rotor bar failure from low-frequency load variation for DTC drives remains unexplored. Experimental results show that by using the proposed method broken rotor bar failure can be reliably detected in the presence of low-frequency load torque oscillation in DTC induction motor drives.
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Tan, Xing Wen. "Study on the Online Diagnosis System of Induction Motor with Broken Bar Fault Based on LabVIEW." Applied Mechanics and Materials 246-247 (December 2012): 765–71. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.765.

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This paper addressed a new approach of online diagnosis of induction motor with broken bar fault based on advanced digital filtering, ZOOM-FFT and acquiring slip by Rotor Slot Harmonics (RSH) techniques, the slip rate is accurately estimated from the precise measurements of the harmonic components of rotor and the power supply frequency, which enables us find the characteristic spectrum of a rotor with broken bar from the stator current spectrum. Thus, the motor broken bar fault can be detected by checking the existence of the characteristic spectrum. The proposed method overcomes the drawback of traditional current spectral analysis approaches. In particular, this paper addresses the problem that the side lobe spectral components are covered by the fundamental frequency and the noises. And the reliability of the fault detection method is improved. The experiment results have shown that the improved method is able to detect small broken rotor bar fault with good application value.
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Salah, Lachtar, Ghoggal Adel, Koussa Khaled, Bouraiou Ahmed, and Attoui Issam. "Broken rotor bar fault diagnostic for DTC Fed induction motor using stator instantaneous complex apparent power envelope signature analysis." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 3 (2019): 1187. http://dx.doi.org/10.11591/ijpeds.v10.i3.pp1187-1196.

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The broken rotor bar is an unexpected fault and a common cause of induction motor failures that threaten the structural integrity of electric machines. In this paper, a new approach to a broken rotor bar diagnosis, without slip estimation, based on the envelope of the stator instantaneous complex apparent power (SICAP) is proposed. The envelope is obtained from the SICAP modulation and then transferred to a computer for monitoring the characteristic frequency and its amplitude using the Fast Fourier Transform (FFT). For this purpose, the winding function approach (WFA) is used to simulate the broken rotor bar occurrence in a squirrel cage induction motor (SCIM) fed on direct torque control (DTC). The obtained simulation results confirm the interest and efficiency of the proposed technique. Even when the induction motor is operating at the no-load level condition, the proposed method is also efficient to detect the broken rotor bar fault at low slip.
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Lachtar, Salah, Adel Ghoggal, Khaled Koussa, Ahmed Bouraiou, and Issam Attoui. "Broken rotor bar fault diagnostic for DTC fed induction motor using stator instantaneous complex apparent power envelope signature analysis." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 3 (2019): 1187–96. https://doi.org/10.11591/ijpeds.v10.i3.pp1187-1196.

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The broken rotor bar is an unexpected fault and a common cause of induction motor failures that threaten the structural integrity of electric machines. In this paper, a new approach to a broken rotor bar diagnosis, without slip estimation, based on the envelope of the stator instantaneous complex apparent power (SICAP) is proposed. The envelope is obtained from the SICAP modulation and then transferred to a computer for monitoring the characteristic frequency and its amplitude using the Fast Fourier Transform (FFT). For this purpose, the winding function approach (WFA) is used to simulate the broken rotor bar occurrence in a squirrel cage induction motor (SCIM) fed on direct torque control (DTC). The obtained simulation results confirm the interest and efficiency of the proposed technique. Even when the induction motor is operating at the no-load level condition, the proposed method is also efficient to detect the broken rotor bar fault at low slip
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Sinaga, Robert, Julieta Christy, Donatus Dahang, et al. "Pengaruh Modifikasi dan Jumlah Alur dan Kecepatan Putar Rotor Bar Terhadap Produktivitas dan Hasil Pemecah Kemiri Sistem Ripple Mill." JURNAL AGROTEKNOSAINS 6, no. 1 (2022): 65. http://dx.doi.org/10.36764/ja.v6i1.749.

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Advanced knowledge of candlenut deshelling machine is needed to obtain optimal whole round kernel. The objective of this study was to modify the rotor bar on a candlenut deshelling machine with a ripple mill system. The method was engineering or modification, were made in the form of processes and products. The number of grooves on the rotor bar was modified became 2, 4 and 6. Rotation speed of the rotor bar were varied to 15 rpm, 18.75 and 25 rpm. The number of grooves gave a very significant effect on the capacity of the machine, whole round kernel, broken kernel, sticky kernel and unbroken seeds. The rotational speed of the rotor bar gave a very significant effect on the capacity of the machine, broken kernel, and sticky kernel, and gave an insignificant effect on whole round kernel and broken kernel. Interaction between the number of grooves and the rotational speed of the rotor bar gave a very significant effect on the capacity of the machine and the sticky kernel and insignificant effect on the whole round kernel, crushed kernel and unbroken seeds.
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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 (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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Dissertations / Theses on the topic "Broken rotor bar"

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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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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.<br>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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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.<br>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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Vedreño, Santos Francisco Jose. "Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions." Doctoral thesis, Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/34177.

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Tradicionalmente, la detección de faltas en máquinas eléctricas se basa en el uso de la Transformada Rápida de Fourier ya que la mayoría de las faltas pueden ser diagnosticadas con ella con seguridad si las máquinas operan en condiciones de régimen estacionario durante un intervalo de tiempo razonable. Sin embargo, para aplicaciones en las que las máquinas operan en condiciones de carga y velocidad fluctuantes (condiciones no estacionarias) como por ejemplo los aerogeneradores, el uso de la Transformada Rápida de Fourier debe ser reemplazado por otras técnicas. La presente tesis desarrolla una nueva metodología para el diagnóstico de máquinas de inducción de rotor de jaula y rotor bobinado operando en condiciones no estacionarias, basada en el análisis de las componentes de falta de las corrientes en el plano deslizamiento frecuencia. La técnica es aplicada al diagnóstico de asimetrías estatóricas, rotóricas y también para la falta de excentricidad mixta. El diagnóstico de las máquinas eléctricas en el dominio deslizamiento-frecuencia confiere un carácter universal a la metodología ya que puede diagnosticar máquinas eléctricas independientemente de sus características, del modo en el que la velocidad de la máquina varía y de su modo de funcionamiento (motor o generador). El desarrollo de la metodología conlleva las siguientes etapas: (i) Caracterización de las evoluciones de las componentes de falta de asimetría estatórica, rotórica y excentricidad mixta para las máquinas de inducción de rotores de jaula y bobinados en función de la velocidad (deslizamiento) y la frecuencia de alimentación de la red a la que está conectada la máquina. (ii) Debido a la importancia del procesado de la señal, se realiza una introducción a los conceptos básicos del procesado de señal antes de centrarse en las técnicas actuales de procesado de señal para el diagnóstico de máquinas eléctricas. (iii) La extracción de las componentes de falta se lleva a cabo a través de tres técnicas de filtrado diferentes: filtros basados en la Transformada Discreta Wavelet, en la Transformada Wavelet Packet y con una nueva técnica de filtrado propuesta en esta tesis, el Filtrado Espectral. Las dos primeras técnicas de filtrado extraen las componentes de falta en el dominio del tiempo mientras que la nueva técnica de filtrado realiza la extracción en el dominio de la frecuencia. (iv) La extracción de las componentes de falta, en algunos casos, conlleva el desplazamiento de la frecuencia de las componentes de falta. El desplazamiento de la frecuencia se realiza a través de dos técnicas: el Teorema del Desplazamiento de la Frecuencia y la Transformada Hilbert. (v) A diferencia de otras técnicas ya desarrolladas, la metodología propuesta no se basa exclusivamente en el cálculo de la energía de la componente de falta sino que también estudia la evolución de la frecuencia instantánea de ellas, calculándola a través de dos técnicas diferentes (la Transformada Hilbert y el operador Teager-Kaiser), frente al deslizamiento. La representación de la frecuencia instantánea frente al deslizamiento elimina la posibilidad de diagnósticos falsos positivos mejorando la precisión y la calidad del diagnóstico. Además, la representación de la frecuencia instantánea frente al deslizamiento permite realizar diagnósticos cualitativos que son rápidos y requieren bajos requisitos computacionales. (vi) Finalmente, debido a la importancia de la automatización de los procesos industriales y para evitar la posible divergencia presente en el diagnóstico cualitativo, tres parámetros objetivos de diagnóstico son desarrollados: el parámetro de la energía, el coeficiente de similitud y los parámetros de regresión. El parámetro de la energía cuantifica la severidad de la falta según su valor y es calculado en el dominio del tiempo y en el dominio de la frecuencia (consecuencia de la extracción de las componentes de falta en el dominio de la frecuencia). El coeficiente de similitud y los parámetros de regresión son parámetros objetivos que permiten descartar diagnósticos falsos positivos aumentando la robustez de la metodología propuesta. La metodología de diagnóstico propuesta se valida experimentalmente para las faltas de asimetría estatórica y rotórica y para el fallo de excentricidad mixta en máquinas de inducción de rotor de jaula y rotor bobinado alimentadas desde la red eléctrica y desde convertidores de frecuencia en condiciones no estacionarias estocásticas.<br>Vedreño Santos, FJ. (2013). Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34177<br>TESIS
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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.<br>Thesis (M.Eng.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2008
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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.<br>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.<br>http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1298314<br>Thesis (Ph.D.)-- University of Adelaide, School of Electrical and Electronic Engineering, 2008
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Book chapters on the topic "Broken rotor bar"

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

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Berenji, Amirhossein, Zahra Taghiyarrenani, and Sławomir Nowaczyk. "curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23633-4_28.

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Arabaci, Hayri, and Osman Bilgin. "Efficiency Analysis of Submersible Induction Motor with Broken Rotor Bar." In Transactions on Engineering Technologies. 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. 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. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93817-8_52.

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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. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9905-4_2.

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Zaniani, Ali Amiri, Dong Zhen, Haiyang Li, and Yinghang He. "A Kalman Filter Based Deep Learning Autoencoder for Induction Motor Broken Rotor Bar Diagnosis." In Mechanisms and Machine Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26193-0_53.

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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. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9239-0_39.

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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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_59.

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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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_49.

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Conference papers on the topic "Broken rotor bar"

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Koveshnikov, Semen, Nada El Bouharrouti, Karolina Kudelina, Usman Muhammad Naseer, Toomas Vaimann, and Anouar Belahcen. "Broken Rotor Bar Fault Detection Using Machine Learning: Optimal Frequency Resolution." In 2024 International Conference on Electrical Machines (ICEM). IEEE, 2024. http://dx.doi.org/10.1109/icem60801.2024.10700228.

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Shestakov, Alexander L., Dmitrii V. Galyshev, Victoria A. Eremeeva, and Olga L. Ibryaeva. "Broken Rotor Bar Fault Diagnosis Using Spectrum Distortion for Data Augmentation." In 2025 27th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2025. https://doi.org/10.1109/dspa64310.2025.10977924.

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Athulgop, P., and S. Kumaravel. "Multi-Observer Based Broken Rotor Bar Detection Algorithm for Induction Motor Traction Application." In 2024 IEEE Calcutta Conference (CALCON). IEEE, 2024. https://doi.org/10.1109/calcon63337.2024.10914127.

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zhang, fengyu. "Fault diagnosis of induction motor rotor broken bar based on GWO-SVMD-1D CNN." In 4th International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024), edited by Wenfeng Hu, Wennian Yu, and Aniruddha Bhattacharjya. SPIE, 2025. https://doi.org/10.1117/12.3054270.

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Istenes, György, and Krisztián Horváth. "FEM–Based Analysis of Induction Machine Broken Rotor Bar Detection Using Extended Kalman Filter." In 2024 23rd International Symposium on Electrical Apparatus and Technologies (SIELA). IEEE, 2024. http://dx.doi.org/10.1109/siela61056.2024.10637867.

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Zhou, Jian, Limin Cheng, Chengju Dong, Wensheng Yang, Xiaojian Ding, and Chunyang Ji. "Diagnosis method of rotor broken bar based on adaptive filtering and phase difference algorithm." In International Conference on Signal Processing and Neural Network Application (SPNNA 2024), edited by Tao Lei and Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3065206.

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Samsi, Rohan, Jeffrey Mayer, and Asok Ray. "Broken Rotor Bar detection using Symbolic Wavelet Analysis." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377822.

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Vico, J., I. Voloh, Zhiying Zhang, D. Stankovic, and D. Swigost. "Enhanced algorithm for motor rotor broken bar detection." In 5th IET International Conference on Power Electronics, Machines and Drives (PEMD 2010). Institution of Engineering and Technology, 2010. http://dx.doi.org/10.1049/cp.2010.0031.

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Vico, Jakov, Ilia Voloh, Dragan Stankovic, and Zhiying Zhang. "Enhanced algorithm for motor rotor broken bar detection." In 2010 56th IEEE Pulp and Paper Industry Conference - PPIC. IEEE, 2010. http://dx.doi.org/10.1109/papcon.2010.5556515.

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Stankovic, Dragan, Zhiying Zhang, Ilia Voloh, et al. "Enhanced algorithm for motor rotor broken bar detection." In 2010 63rd Annual Conference for Protective Relay Engineers. IEEE, 2010. http://dx.doi.org/10.1109/cpre.2010.5469512.

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