Journal articles on the topic 'Broken rotor bar faults'
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1
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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Wu, Yucai, Shuqiong Sun, Qingfei An, and Xu Lie. "Treatment Strategy Research on a Squirrel-Cage Induction Motor with Broken Rotor Bar Faults." Sensors 22, no. 12 (June 8, 2022): 4345. http://dx.doi.org/10.3390/s22124345.
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Squirrel-cage induction motors are increasingly displaying a broken rotor bar fault, which represents both a technical problem and an economic problem. After confirming that the broken rotor bars do not affect the normal start-up and basic working performance of the squirrel-cage induction motor, this paper focuses on the loss and efficiency changes of the motor brought about by the broken rotor bar fault. Using finite element simulation and experimentation, various losses like stator copper loss, iron loss, rotor copper loss, mechanical loss and additional losses, total loss and efficiency are obtained. By combining price and cost factors, the cost-effective measures that can be taken after the occurrence of different degrees of broken bars are evaluated here to provide guidance for correctly handling this problem.
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Altaf, S., M. S. Mehmood, and M. W. Soomro. "Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment." Journal of Engineering Sciences 6, no. 2 (2019): d1—d8. http://dx.doi.org/10.21272/jes.2019.6(2).d1.
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Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.
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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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Tvorić, Stjepan, Miroslav Petrinić, Ante Elez, and Mario Brčić. "STATIC ECCENTRICITY FAULT DETECTION METHOD FOR ELECTRICAL ROTATING MACHINES BASED ON THE MAGNETIC FIELD ANALYSIS IN THE AIR GAP BY MEASURING COILS." Journal of Energy - Energija 69, no. 4 (December 30, 2020): 3–7. http://dx.doi.org/10.37798/202069451.
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Electrical rotating machines have a great economic significance as they enable conversion of energy between mechanical and electrical state. Reliability and operation safety of these machines can be greatly improved by implementation of continuous condition monitoring and supervisory systems. Especially important feature of such systems is the ability of early fault detection. For this reason, several methods for detection and diagnosis of the machine faults have been developed and designed. As fault detection methods can largely differ in the types of detectable faults, machine adoptability and price of the system, a novel method was developed that can be used for cost-effective detection of various faults of electrical machine. Machine fault detection technique presented in this paper is based on the measurement of magnetic field in the air gap. Numerous studies have proven that crucial information about the machine condition can be determined based on measurement and analysis of the magnetic field in the air gap. It has also been confirmed that analysis of the air gap magnetic field can be used to detect, diagnose and recognize various electrical faults in their very early stage. Proposed method of positioning and installation of the measuring coils on ferromagnetic core parts within the air gap region of the machine enables differentiation of various faults. Furthermore, different faults can be detected if measuring coils are placed on the stator teeth then when placed on the rotor side. The paper presents method on how to analyse and process the measured voltages acquired from measuring coils placed within the machine, especially in the case of rotor static eccentricity detection. The methodology is explained by means of finite element method (FEM) calculations and verified by measurements that were performed on the induction machine. FEM calculation model was used to predict measurement coil output of the induction motor for healthy and various faulty states (at different amounts of static eccentricity). These results were then confirmed by measurements performed in the laboratory on the induction traction motor that was specially modified to enable measurements of faulty operation states of the machine. Measurements comprised of several machine fault conditions broken one rotor bar, broken multiple rotor bars, broken rotor end ring and various levels of rotor static eccentricity.
Other methods used for faults detection are primarily based on the monitoring of quantities such as current and vibration and their harmonic analysis. This new system is based on the tracing the changes of induced voltage of the measuring coils installed on the stator teeth. Faults can be detected and differentiated based on RMS value of these voltages and the number of voltage spikes of voltage waveform i.e. without the need of harmonic analyses. If these coils are installed on the rotor it is possible to detect the stator winding faults in a similar manner.
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Asad, Bilal, Toomas Vaimann, Anton Rassõlkin, Ants Kallaste, and Anouar Belahcen. "A Survey of Broken Rotor Bar Fault Diagnostic Methods of Induction Motor." Electrical, Control and Communication Engineering 14, no. 2 (December 1, 2018): 117–24. http://dx.doi.org/10.2478/ecce-2018-0014.
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AbstractElectrical machines, induction motors in particular, play a key role in domestic and industrial applications. They act as a work horse in almost every industry and are responsible for a big proportion of total generated electricity consumption worldwide. The faults in induction motors are degenerative in nature and can lead to a catastrophic situation if not diagnosed earlier. The failures can cause considerable financial loss in the form of unexpected downtime. Broken rotor bar is a very common and frequently occurring fault in most of industrial induction motors. To select a better, more accurate and reliable fault diagnostic technique, this paper presents a comprehensive literature survey on the existing motor current signature analysis (MCSA) based fault diagnostic techniques. Different well-known MCSA based fault diagnostic techniques are summarized in the form of basic theories, considering complexity of their implementation, merits and demerits.
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Amanuel, Thomas, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, and Weldekidan Bahlibi. "Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor." March 2021 3, no. 1 (April 19, 2021): 61–76. http://dx.doi.org/10.36548/jei.2021.1.006.
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Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.
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Fares, Noureddine, Zoubir Aoulmi, Tawfik Thelaidjia, and Djamel Ounnas. "Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine." European Journal of Electrical Engineering 24, no. 4 (August 31, 2022): 171–83. http://dx.doi.org/10.18280/ejee.240402.
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Induction machine health monitoring is considered a developing technology for the online detection of faults that occur even at the initial stage. The objective of this study is to present an artificial intelligence (AI) technique for the detection and localization of adjacent and distant broken bar faults in the induction machine, through a multi-winding model for the simulation of these cases. In this work, it was found that the application of Artificial Neural Networks (ANN) based on Mean Squared Error (MSE) and Random Forest (decision tree) plays an important role in detecting and locating defaults. The stator current signal Ias of a motor in the dynamic state was acquired from a healthy and faulty motor with a broken rotor bar fault. 9 statistical features and 8 wavelet packet parameters are extracted from the stator current signal. These features were employed as an input vector to train and test the ANN and random fores29t and determine whether the motor was running under normal conditions or defective. For optimizing the rotor bar defect classification procedure, feature selection algorithms are adopted, such as BBAT and BPSO. For feature reduction, we used the principal component analysis (PCA) algorithm, to reduce the number of features. The results showed that the random forest classifier based on statistical parameters and wavelet packet parameters followed by PCA can detect the defective with high accuracy (98.3333%) compared to other methods.
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Chehaidia, Seif Eddine, Hakima Cherif, Musfer Alraddadi, Mohamed Ibrahim Mosaad, and Abdelaziz Mahmoud Bouchelaghem. "Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System." Energies 15, no. 18 (September 15, 2022): 6746. http://dx.doi.org/10.3390/en15186746.
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Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP.
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Hou, Xin Guo, and Fan Bu. "The Motor Rotor Fault Diagnosis Method Based on Linear Mixing Blind Separation Model." Applied Mechanics and Materials 602-605 (August 2014): 2420–25. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2420.
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The detection precision of fault diagnosis based on frequency spectral analysis of stator current is easily restricted by noise jamming and frequency resolution. A fault diagnosis method for induction motor based on linear mixing model is proposed to resolve this problem. The fault characteristic signals are separated from the motor stator current by Fast-ICA algorithm and its amplitude is calculated according to the estimated mixing matrix. The fault diagnosis is achieved by difference of the amplitude on the normal state and the fault state of the motor. In this paper, the fault diagnosis of the broken rotor bars faults is used as an example to explain the conclusion as mentioned. Experiment result shows that the broken-rotor-bar fault can be diagnosed by the algorithm with better effect on the condition of short data block.
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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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Halder, Sudip, Sunil Bhat, Daria Zychma, and Pawel Sowa. "Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor—A Review." Energies 15, no. 22 (November 16, 2022): 8569. http://dx.doi.org/10.3390/en15228569.
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The most often used motor in commercial drives is the induction motor. While the induction motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses can result in defects. Therefore, many researchers who are involved in condition monitoring have been interested in the development of reliable and efficient fault diagnostic technologies. This paper’s goal is to provide an overview of available fault detection methods for the broken rotor bar problem, one of several defects associated to induction motors. Despite the fact that it is less common than bearing or insulator failure, this fault may cause electrical machines to fail catastrophically. It can be quite harmful, especially in large motors, and it can develop as a result of manufacturing faults, repeated starting of the machine, mechanical stress, and thermal stress. Hence, a review on rotor defect diagnosis was conducted. In order to confirm rotor bar fracture, this research provides probable defect signatures that can be extracted from the current signal. Each defect signature is reported according to (a) loading level, (b) the number of BRBs, (c) validation, and (d) methodologies.
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Souza, Mateus Ventura, José Claudeni Oliveira Lima, Alexandre Magno Pinto Roque, and Douglas Bressan Riffel. "A Novel Algorithm to Detect Broken Bars in Induction Motors." Machines 9, no. 11 (October 26, 2021): 250. http://dx.doi.org/10.3390/machines9110250.
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A new algorithm is proposed in order to detect and quantify partially broken bars in induction motors during start-up without load. In the qualification process, no threshold is used. It uses the principle of the harmonic generated by the broken bar in the stator current, it should vary with the slip to confirm the failure and provide more security in the diagnosis. A severity index is also proposed, based on the maximum peaks of the Teager energy operator of the Gaussian filter applied in the stator current signal. Experimental data were used to validate the algorithm, comparing rotors manufactured with one partially bar, one failed bar, and two completely failed bars, arranged in a variety of ways. The results show that the algorithm qualifies correctly the faulty bar, even for a partially broken bar. In the quantification phase, the severity index of the fault shows the higher sensibility in comparison to the state-of-the-art. Its value for a 3 HP motor is: 8.837 × 10−10 for a healthy rotor, 2.553 × 10−8 for a partially broken bar, and 4.058 × 10−7 for one broken bar.
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AL-Yoonus, Marwan Abdulkhaleq, and Omar Sharaf Al-deen Alyozbaky. "Detection of internal and external faults of single-phase induction motor using current signature." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 2830. http://dx.doi.org/10.11591/ijece.v11i4.pp2830-2841.
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<span>The main aim of this work is to analyze the input current waveform for a single-phase induction capacitor-run motor (SIMCR) to detect the faults. Internal and external faults were applied to the SIMCR and the current was measured. An armature (broken rotor bar) and bearing faults were applied to the SIMCR. A microcontroller was used to record the motor current signal and MATLAB software was used to analyze it with the different types of fault with varying load operations. Various values of the running capacitor were used to investigate the effect of these values on the wave-current shape. Total harmonic distortion (THD) for the voltage and current was measured. A deep demonstration of the experimental results was also provided for a better understanding of the mechanisms of bearing and armature faults (broken rotor bars) and the vibration was recorded. Spectral and power analyses revealed the difference in total harmonic distortion. The proposed method in this paper can be used in various industrial applications and this technique is quite cheap and helps most of the researchers and very effectual.</span>
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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 (September 1, 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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Sinha, Ashish Kumar, Sukanta Das, and Tarun Kumar Chatterjee. "Empirical relation for broken bar determination in SCIM." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 1 (January 2, 2018): 242–65. http://dx.doi.org/10.1108/compel-11-2016-0515.
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Purpose Condition monitoring of squirrel cage induction motors (SCIMs) is indispensible for achieving fault-free working environment. As broken rotor bars (BRBs) are one of the more frequent faults in a SCIM especially where direct-on-line starting is indispensible, as in underground mines, a priori knowledge of fault severity in terms of the number of BRBs assists in effective fault monitoring. In this regard, this paper aims to propose a unique empirical relation to facilitate the determination of number of BRB. Design/methodology/approach Fast Fourier transform is used to obtain fault sideband amplitudes under varying number of BRBs and load torque for 5.5 kW, 7.5 kW, 10 kW, three-phase, 415 V, 50 Hz SCIMs in MATLAB/Simulink. The nature of variation is decided by an appropriate curve fitting technique for comprehending a unique empirical relation. The proposed empirical relation is validated by bootstrapping and z-test. Furthermore, hardware validation is done using 1 kW laboratory prototype with Labview interface. Findings The analytical study reveals the dependence of lower and upper sideband amplitudes on the number of BRBs, load torque and machine rating. Therefore, fault severity in terms of number of BRBs is accurately calculated using the proposed empirical relation if load torque, machine rating and amplitudes of lower and upper sidebands are known. Originality/value The unique empirical relation proposed in the present work provides accurate knowledge of fault severity in terms of the number of BRBs. This facilitates maintenance scheduling which shall reduce effective downtime and improve production.
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Pineda-Sanchez, Manuel, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Bano, Martin Riera-Guasp, and Juan Perez-Cruz. "Partial Inductance Model of Induction Machines for Fault Diagnosis." Sensors 18, no. 7 (July 18, 2018): 2340. http://dx.doi.org/10.3390/s18072340.
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The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars.
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Amanuel, Thomas, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, and Weldekidan Bahlibi. "Design of Vibration Frequency Method with Fine-Tuned Factor for Fault Detection of Three Phase Induction Motor." Journal of Innovative Image Processing 3, no. 1 (April 12, 2021): 52–65. http://dx.doi.org/10.36548/jiip.2021.1.005.
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This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.
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Nemec, Mitja, Vanja Ambrožič, Rastko Fišer, David Nedeljković, and Klemen Drobnič. "Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring." Energies 12, no. 5 (February 27, 2019): 794. http://dx.doi.org/10.3390/en12050794.
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This paper presents a method for the detection of broken rotor bars in an induction motor. After introducing a simplified dynamic model of an induction motor with broken cage bars in a rotor field reference frame which allows for observation of its internal states, a fault detection algorithm is proposed. Two different motor estimation models are used, and the difference between their rotor flux angles is extracted. A particular frequency component in this signal appears only in the case of broken rotor bars. Consequently, the proposed algorithm is robust enough to load oscillations and/or machine temperature change, and also indicates the fault severity. The method has been verified at different operating points by simulations as well as experimentally. The fault detection is reliable even in cases where traditional methods give ambiguous verdicts.
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Ramu, Senthil Kumar, Gerald Christopher Raj Irudayaraj, Gunapriya Devarajan, V. Indragandhi, V. Subramaniyaswamy, and J. Sam Alaric. "Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications." International Transactions on Electrical Energy Systems 2022 (September 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/9474640.
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The induction motor (IM) defect diagnosis has been an important field of research in recent years. The development in control circuits for IM has piqued the interest of industrialists and researchers. This paper presents a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM. The fast Fourier transform (FFT) extracts the signal’s amplitude and frequency components, while the discrete wavelet transform (DWT) decomposes it. In this paper, the energy estimation for each level of breakdown and the method to overcome the diagnose faults are explained. The EEV of the motor current of the signal determines the fault’s severity and provides a better method for identifying the faults. The usage of a single current sensor is a gain of this technology. With a fluctuating load, we can identify the issue and the number of broken bars via online. After processing of DWT, the faulty BRB’s stator current signal is suppressed to 91% in amplitude when compared to existing techniques. Simulation and experimental results have proved that the proposed method’s stability, durability, and resilience.
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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 (July 7, 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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Wu, Qiao Shan. "Breaks Strip Breakdown with the Parameter Identification Law Diagnosis Mouse Cage Asynchronous Motor Rotor." Applied Mechanics and Materials 291-294 (February 2013): 2549–52. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2549.
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According to the three-phase squirrel-cage asynchronous motor rotor broken bar fault, presented with parameters identification method of asynchronous motor parameter identification to monitoring and diagnosis of rotor resistance variation of rotor bar breaking principle. Choose a three-phase squirrel-cage asynchronous motor in three working points of experiments, results show that the method is correct and feasible. This method is based on the three-phase squirrel-cage asynchronous motor steady state model parameter equation, using the method of least squares identification parameters, and consider the effects of temperature on the parameters, by the parameter variation in diagnosis of broken rotor bar fault. Advantages of simple scientific method, this method can also be used in conjunction with other methods and, on the three-phase squirrel-cage asynchronous motor rotor has no fault diagnosis.
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Hwang, Don-Ha, Young-Woo Youn, Jong-Ho Sun, and Yong-Hwa Kim. "Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors." Journal of Electrical Engineering and Technology 9, no. 1 (January 1, 2014): 37–44. http://dx.doi.org/10.5370/jeet.2014.9.1.037.
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Li, Wang, Zhen, Gu, and Ball. "Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors." Energies 12, no. 23 (November 21, 2019): 4437. http://dx.doi.org/10.3390/en12234437.
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Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.
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Kathir, I., S. Balakrishnan, and B. V. Manikandan. "Broken Rotor Bar Detection Using High Frequency Loss Calculation of Induction Motor." Applied Mechanics and Materials 573 (June 2014): 728–33. http://dx.doi.org/10.4028/www.scientific.net/amm.573.728.
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This paper proposes a technique for the identification of defects of three-phase squirrel cage induction motors. Simulations were performed using ANSYS finite element software package to obtain the flux density waveform in the air gap. Broken rotor bar fault was simulated by breaking rotor bars to see how the flux density is affected. In this paper, a new approach for the identification of broken rotor bar based on the calculation of high-frequency losses in induction motors is presented. The approach presented in this paper requires little time for loss calculation and fault identification.
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Shi, X. J., C. X. Zhang, and Jun Peng Shao. "Sensorless Detection and Diagnosis Method for Induction Motor and its Driven Equipment." Key Engineering Materials 392-394 (October 2008): 98–102. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.98.
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The mechanical equipments driven by induction motor are widely used in manufacturing. Aiming at this type of equipment, some holes are advisedly drilled on the bars to simulate the broken bar faults of the motor, and with on-off loads changes of output circuit of the load generator, forced torsional vibration of rotor was generated. Using the above simulation test ways, the typical faults of motor and its driven equipment are tested and analyzed. In addition, an signal analysis method using Hilbert conversion envelope spectrum and real modulation zoom envelope spectrum is proposed, this method can effectively extract the faults information of stator current, reject the useless power frequency. The experimental results indicate that: this method can identify not only the faults of the motor itself, but also municipal fault types of the motor’s driven equipment. Especially, through the contrastive experiments on the unbalance and the torsional vibration of the rotor, the conclusion is made that the method is more sensitive to torsional vibration detection. Also, it develops a new direction for the application and the research of sensorless detection and diagnosis method.
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Xie, Ying, Jinpeng Guo, Peng Chen, and Zhiwei Li. "Coupled Fluid-Thermal Analysis for Induction Motors with Broken Bars Operating under the Rated Load." Energies 11, no. 8 (August 3, 2018): 2024. http://dx.doi.org/10.3390/en11082024.
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Thermal stress of the rotor in a squirrel cage induction motor is generated due to the temperature rise, it is also one of the factors causing the broken bar fault because the structure of the rotor would be destroyed if the stress of the rotor bars exceed the strength limit. The coupled fluid-thermal analysis for the induction motor with healthy and broken bar rotors is performed in this paper. Much concern has been committed to establishment of the fluid model on the basis of computational fluid dynamic (CFD) theory. The heat field of the prototypes is analysed so that the effect of the asymmetrical rotor on the motor heat performance can be investigated in depth. Eventually, the efficiency of the presented model and method, for the totally enclosed fan cooled (TEFC) induction motor, can be verified through experimental results. In addition, this paper reports a quantitative analysis of the heat flux distribution of the fault rotor, and the heat flux density of the bars is investigated in detail. Then, the part most likely to break in the rotor as a result of the thermal load is identified.
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Navarro-Navarro, Angela, Israel Zamudio-Ramirez, Vicente Biot-Monterde, Roque A. Osornio-Rios, and Jose A. Antonino-Daviu. "Current and Stray Flux Combined Analysis for the Automatic Detection of Rotor Faults in Soft-Started Induction Motors." Energies 15, no. 7 (March 29, 2022): 2511. http://dx.doi.org/10.3390/en15072511.
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Induction motors (IMs) have been extensively used for driving a wide variety of processes in several industries. Their excellent performance, capabilities and robustness explain their extensive use in several industrial applications. However, despite their robustness, IMs are susceptible to failure, with broken rotor bars (BRB) being one of the potential faults. These types of faults usually occur due to the high current amplitude flowing in the bars during the starting transient. Currently, soft-starters have been used in order to reduce the negative effects and stresses developed during the starting. However, the addition of these devices makes the fault diagnosis a complex and sometimes erratic task, since the typical fault-related patterns evolutions are usually irregular, depending on particular aspects that may change according to the technology implemented by the soft-starter. This paper proposes a novel methodology for the automatic detection of BRB in IMs under the influence of soft-starters. The proposal relies on the combined analysis of current and stray flux signals by means of suitable indicators proposed here, and their fusion through a linear discriminant analysis (LDA). Finally, the LDA output is used to train a feed-forward neural network (FFNN) to automatically detect the severity of the failure, namely: a healthy motor, one broken rotor bar, and two broken rotor bars. The proposal is validated under a testbench consisting of a kinematic chain driven by a 1.1 kW IM and using four different models of soft-starters. The obtained results demonstrate the capabilities of the proposal, obtaining a correct classification rate (94.4% for the worst case).
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Trujillo Guajardo, Luis Alonso, Miguel Angel Platas Garza, Johnny Rodríguez Maldonado, Mario Alberto González Vázquez, Luis Humberto Rodríguez Alfaro, and Fernando Salinas Salinas. "Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors." Energies 15, no. 10 (May 11, 2022): 3513. http://dx.doi.org/10.3390/en15103513.
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This article presents an evaluation of Prony method and its implementation considerations for motor current signal analysis diagnostics in rotor cage induction motors. The broken rotor bar fault signature in current signals is evaluated using Prony method, where its advantages in comparison with fast Fourier transform are presented. The broken rotor bar fault signature could occur during the life cycle operation of induction motors, so that is why an effective early detection estimation technique of this fault could prevent an insulation failure or heavy damage, leaving the motor out of service. First, an overview of cage winding defects in rotor cage induction motors is presented. Next, Prony method and its considerations for the implementation in current signature analysis are described. Then, the performance of Prony method using numerical simulations is evaluated. Lastly, an assessment of Prony method as a tool for current signal analysis diagnostics is performed using a laboratory test system where real signals of an induction motor with broken rotor bar operated with/without a variable frequency drive are analyzed. The summary results of the estimation (amplitudes and frequencies) are presented in the results and discussion section.
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Huang, Baoshan, Guojin Feng, Xiaoli Tang, James Xi Gu, Guanghua Xu, Robert Cattley, Fengshou Gu, and Andrew D. Ball. "A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems." Energies 12, no. 8 (April 15, 2019): 1438. http://dx.doi.org/10.3390/en12081438.
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This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression.
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En, De, Xiao Long Shi, Huang He Wei, Na Na Wei, and Chang Sheng Zhou. "Adaptive Filter with Multiple-Scale Decomposition Rotor Broken Bars in Induction Fault Diagnosis." Applied Mechanics and Materials 273 (January 2013): 428–33. http://dx.doi.org/10.4028/www.scientific.net/amm.273.428.
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There is a very small additional current component of frequency in the stator current signal when motor has broken rotor bars.So adaptive notch filter is applied to process the signals of the stator current in induction motors.The variable step size LMS algorithm and the multiple-scale wavelet transform are merged into the adaptive filtering system.A method is proposed, that is a LMS adaptive filtering algorithm with modified variable step size based on multiple-scale wavelet transform(MSWT-MVSS-LMS).It can eliminate interference from power frequency component to frequency component of broken rotor bar and achieve precise identification to frequency component of broken rotor bar from FFT.The result is a great help to extract the feature component of rotor fault and improve the sensitivity of fault diagnosis.The simulation show that the method is valid and effective.
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Sabbaghian-Bidgoli, F., and J. Poshtan. "Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform." Fluctuation and Noise Letters 17, no. 02 (May 2, 2018): 1850012. http://dx.doi.org/10.1142/s0219477518500128.
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Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.
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Asad, Bilal, Toomas Vaimann, Anouar Belahcen, Ants Kallaste, Anton Rassõlkin, Payam Shams Ghafarokhi, and Karolina Kudelina. "Transient Modeling and Recovery of Non-Stationary Fault Signature for Condition Monitoring of Induction Motors." Applied Sciences 11, no. 6 (March 21, 2021): 2806. http://dx.doi.org/10.3390/app11062806.
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This paper presents the modeling and the broken rotor bar fault diagnostics by time–frequency analysis of the motor current under an extended startup transient time. The transient current-based nonstationary signal is retrieved and investigated for its time–frequency response to segregate the rotor faults and spatial harmonics. For studying the effect of reduced voltage on various parameters and the theoretical definition of the fault patterns, the winding function analysis (WFA)-based model is presented first. Moreover, an algorithm to improve the spectrum legibility is proposed. It is shown that by efficient utilization of the attenuation filter and consideration of the area containing the maximum power spectral density, the diagnostic algorithm gives promising results. The results are based on the machine’s analytical model and the measurements taken from the laboratory setup.
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Gangsar, Purushottam, and Rajiv Tiwari. "Effect of noise on support vector machine based fault diagnosis of IM using vibration and current signatures." MATEC Web of Conferences 211 (2018): 03009. http://dx.doi.org/10.1051/matecconf/201821103009.
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This paper analyzes the effect of noise on support vector machine (SVM) based fault diagnosis of IM (IM). For this, a number of mechanical (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor) and electrical faults (broken rotor bar, stator winding fault with two severity levels and phase unbalance with two severity levels) of IM are considered here. The vibration and current signals are used here for the diagnosis. Different experiments were performed in order to generate these signals at various operating condition of IM (Speed and Load). Time domain feature are then extracted from the raw vibration and current signals obtained from the experiments. Then, the noise are added in the raw signals and the same features are extracted from this corrupted signals. The features from the original and corrupted signals are used to feed the classifier. The one-versus-one multiclass SVM are used here to perform multi-fault diagnosis. The comparative analysis of performance of the SVM classifier using data with and without noise is presented.
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Li, Yong Xin, Ya Fei Wang, Xing Lai Ge, and Yang Lu. "Characteristic Performance Analysis of Traction Motor on EMU with Broken Bars Using FEM." Advanced Materials Research 1061-1062 (December 2014): 841–48. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.841.
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The calculation model of motor operating on asymmetric rotor was defined taking a traction squirrel-cage induction motor in Electric Multiple Units (EMU) as sample. Based on the basic principle of numerical analysis, magnetic field in the motor and current in the bar were calculated for both normal motor and broken-bar motor by using the Jmag software, and both the magnetic field variation and current variation in the bar were summed up. According to the EMU’s special control methods, the motor with two broken bars was simulated at different work point, and the characteristic component variation was presented. The works in this paper lay the foundation of diagnosis method for broken rotor bar fault, especially for traction motor in EMU.
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Samir, Hamdani, Touhami Omar, and Ibtiouen Rachid. "Generalized two axes model of a squirrel-cage induction motor for rotor fault diagnosis." Serbian Journal of Electrical Engineering 5, no. 1 (2008): 155–70. http://dx.doi.org/10.2298/sjee0801155s.
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A generalized two axes model of a squirrel-cage induction motor is developed This model is based on a winding function approach and the coupled magnetic circuit theory and takes into account the stator and the rotor asymmetries due to faults. This paper presents a computer simulation and experimental dynamic characteristics for a healthy induction machine, machine with one broken bar and a machine with two broken bars. The results illustrate good agreement between both simulated and experimental results. Also, the power spectral density PSD was performed to obtain a stator current spectrum.
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Bannov, D. M. "ANALYSIS OF METHODS FOR DIAGNOSTICS BROKEN ROTOR BAR OF INDUCTION MOTOR." Electrical and data processing facilities and systems 17, no. 3-4 (2021): 5–23. http://dx.doi.org/10.17122/1999-5458-2021-17-3-4-5-23.
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Relevance Uninterrupted operation of industrial facilities, oil and gas sector, metallurgy, power generation and other industries directly depends on reliable operation of critical mechanisms driven by electromechanical converters, operated as a part of working complexes. Reliability of operation of such mechanisms depends on reliability of all elements that make up the technological process. Inductions motor with squirrel-cage rotor (including high-voltage motors) is one of the most common types of converters of electrical energy into mechanical energy. Its uninterrupted operation directly depends on the reliability of two main elements: stator and rotor. If the causes of induction motor failure due to stator causes (inter-turn, inter-phase and single-phase short circuits) are determined by the provided protections, the damage in the rotor circuit can be implicit and exist for a long time, violating the machine uptime. Also, most of the faults in the inductions motor, leading to an emergency shutdown of technological processes of working complexes have their own history of development. At the moment of occurrence and during the period when the defect does not affect the serviceability of the machine, but its operation becomes critically dangerous, because it is not possible to determine the presence of the defect. So, for example, the breakage of the short-circuited rotor core of a high-voltage induction motor manifests itself at the stage of exit to the air gap at the time of operation, with subsequent damage to the stator winding and the magnetic core. In this case, the existence of this defect took place within a certain period of time, sufficient for its detection by means of diagnostic devices. The article analyzes the currently developed systems for diagnosing the presence of a fault according to various parameters (temperature, noise, vibration, analysis of electrical values consumed) both in the stator and in the rotor. It is determined that the most promising and technically feasible are methods based on the analysis of stator currents. When studying the works in the direction of inductions motor diagnostics it was found that a significant proportion of failures of mechanisms operated in the working complexes due to failure of inductions motor occurs due to broken rotor bar. Aim of research Analyze the existing methods for diagnosing induction motors during operation. Research methods The article used general scientific research methods: the method of analysis of literary sources, the study and generalization of information, comparison, classification. Results The analysis of existing methods of continuous diagnostics of induction motors for internal electrical and mechanical damage is carried out. It has been determined that the most promising from the point of view of economic and technical feasibility are methods based on the analysis of currents consumed by the stator.
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Shnibha, R., A. Albarbar, A. Abouhnik, and G. Ibrahim. "A More Reliable Method for Monitoring the Condition of Three-Phase Induction Motors Based on Their Vibrations." ISRN Mechanical Engineering 2012 (October 18, 2012): 1–9. http://dx.doi.org/10.5402/2012/230314.
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This paper is concerned with accurate, early, and reliable fault diagnosis using an enhanced vibration measurement technique based on short-time Fourier transform. The novelty of this work lies in detecting very low-phase imbalance-related faults. The energy contained within specified frequency bands centred on the rotor frequency and power supply frequency, and their sideband zones were calculated. The technique was firstly demonstrated by simulated signals and then verified by experimental measurements taken from two different-sized test rigs. The first one comprised a 1.1 kW variable speed three-phase induction motor with varying output load (no load, 25%, 50%, 75%, and 100% load). Two types of common faults were introduced: imbalance in one phase as the electrical fault and misalignment of load as the mechanical fault. The second test rig had a 3 kW three-phase induction motor again with varying load, and here the two seeded faults were: phase imbalance and one broken rotor bar. The measured energy levels in the test conditions were found to be affected by type of fault and fault severity. It is concluded that the proposed method offers a potentially reliable and computationally inexpensive condition monitoring tool which can be implemented with real-time monitoring systems.
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Wang, Zuolu, Jie Yang, Haiyang Li, Dong Zhen, Yuandong Xu, and Fengshou Gu. "Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis." Energies 12, no. 17 (August 26, 2019): 3279. http://dx.doi.org/10.3390/en12173279.
Full textAbstract:
Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.
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Ágoston, Katalin. "Studying and Simulating the Influence of the Rotor Fault on Stator Current." Acta Marisiensis. Seria Technologica 17, no. 1 (June 1, 2020): 17–21. http://dx.doi.org/10.2478/amset-2020-0004.
Full textAbstract:
AbstractThis paper presents fault detection techniques, especially the motor current signature analysis (MCSA) which consists of the phase current measurement of the electrical motor’s stator and/or rotor. The motor current signature analysis consists in determining the frequency spectrum (FFT) of the stator current signal and evaluating the relative amplitude of the current harmonics. Sideband frequencies appear in the frequency spectrum of the current, corresponding to each fault. The broken bar is a frequent fault in induction motors with squirrel-cage rotor. It is presented the equivalent circuit for induction motors and the equivalence between the squirrel-cage rotor and the rotor windings. It is also presented an equivalent circuit model for induction motors with squirrel cage rotor, and based on this a Simulink model was developed. It is shown how a broken rotor bar influences the magnetic field around the rotor and through this the stator current. This modification is highlighted through the developed model.
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Delgado-Arredondo, Paulo Antonio, Arturo Garcia-Perez, Daniel Morinigo-Sotelo, Roque Alfredo Osornio-Rios, Juan Gabriel Avina-Cervantes, Horacio Rostro-Gonzalez, and Rene de Jesus Romero-Troncoso. "Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient." Shock and Vibration 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/708034.
Full textAbstract:
Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.