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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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to present a diagnosis technique, for rotor broken bar in double cage induction motor, based on advanced use of wavelet transform analysis. The proposed technique is experimentally validated.

The proposed approach is based on a combined use of frequency sliding and wavelet transform analysis, to isolate the contribution of the rotor fault components issued from vibration signals in a single frequency band.

The proposed technique is reliable for tracking the rotor fault components over time-frequency domain. The quantitative analysis results based on this technique are the proof of its robustness.

The validity of the proposed diagnosis approach is not limited to the analysis under steady-state operating conditions, but also for time-varying conditions where rotor fault components are spread in a wide frequency range.

The developed approach is best suited for automotive or high power traction systems, in which safe-operating and availability are mandatory.

The paper presents a diagnosis technique for rotor broken bar in double cage induction motor base on advanced use of wavelet transform which allows the extraction of the most relevant rotor fault component issued from axial vibration signal and clamping it in a single frequency bandwidth, avoiding confusions with other components and false interpretations.

The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates.

First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs.

The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach.

The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).

The purpose of this paper is to propose applications of advanced signal-processing techniques for the diagnosis and detection of rotor fault in an induction machine. Two techniques are used: spectral analysis techniques and time frequency techniques for the diagnosis of an electrical machine. One is based on the power spectral density estimation techniques, such as periodogram and Welch periodogram. The second method is based on Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energies (HDWT).

First, this paper focused on theoretical development and a comparative study of these signal-processing techniques, which are based on the periodogram, Welch periodogram, HT and the DWT to extract the envelope for the stator current; it is used to compute the energy stored in each decomposition level obtained by the stator current envelope (HDWT). Moreover, the Welch periodogram is applied to obtain the envelope spectrum.

The simulation obtained and the experimental validation results of the proposed methods through MATLAB environment show the effectiveness of the proposed approaches with a good accuracy by power spectral density estimation techniques (periodogram and Welch periodogram). Moreover, the faults are manifested through the appearance of new frequencies components, as well as the envelope for the stator current (HT and DWT). This approach is effective for non-stationary and stationary signal to extract useful information for the detection of broken bar fault.

The current paper proposes a new diagnosis method for the detection and characterization of broken rotor bars defects early; it is founded primarily on theoretical development, and the comparison is based on the power spectral density technique (periodogram and Welch periodogram) and the computation of the energy stored in each decomposition level (precisely the HT and DWT). Moreover, the Welch periodogram is applied to obtain the envelope spectrum. The main advantages of the proposed techniques increase their reliability and availability.

The objective is the application of vibration analysis for the detection and diagnosis of low isolation between the stator coil wind and the voltage phase unbalance in induction motors with different numbers of poles. The purpose of this paper is to provide an approach for maintenance engineers for diagnosis electrical fault through the vibration analyses.

A detailed review of previous work carried out by some researchers and maintenance engineers in the area of machine fault detection is performed. By vibration analysis, the spectra were collected, which used to analyze the failure. Vibration spectra could detect particular characteristic for each fault in an initial condition, so the machine health can be preserved.

Results show the efficiency of the technique of vibration analysis and their relevance to detect and diagnose faults in different induction motors. In this way, it may be included in future predictive maintenance programs.

The paper presents a laboratory investigation carried out through an experimental set-up for the study of fault, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance.

The main contribution of the paper has been the characterization of one more tool that makes the predictive maintenance process more efficient, effective and faster, increasing the reliability and availability of equipment.

This paper presents two modeling methods applied to induction machine study in order to construct a tool for diagnosis purpose. The first method is based on permeance networks using finite element analysis to calculate magnetic equivalent circuit parameters. The second method consists of the elaboration of an electric equivalent circuit obtained from minimal geometrical knowledge on stator and rotor parts of the machine on study. These two methods are presented and their results are compared with respect to the normal and rotor broken bar operation. For this study, a simple structure induction machine with three stator coils and six rotor bars has been investigated. The presented results concern stator currents and electromagnetic torque for the rated speed and the magnitude of the stator current harmonic components have been compared.

This research presents the theory and implementation detail for the extraction of angular speed variations. In particular, band‐pass filtering, frequency shifting and analytic representation are addressed with simple mathematical equations. Finally, some case studies on a 3kW induction motor are investigated for healthy and faulty conditions. Some vital key features and characteristics can be extracted from the angular speed variations. Pole pass speed side bands around the rotor speed can be used as a feature for broken rotor bar faults. The number of poles multiplied by the synchronous running speed cycles and the rise of 100 Hz components are key characteristics for voltage imbalance faults.

The purpose of this paper is to introduce an algorithm based only on local extreme analysis of a time sequence to further the detection and diagnosis of inter-turn short circuits and unbalanced voltage supply using vibration signals.

The upper and lower extreme envelopes from a modulated and oscillatory time sequence present a particular characteristic being of, theoretically, symmetrical versions with regard to amplitude reflection around the time axis. Thus, one may say that they carry the same characteristics in terms of waveforms and, consequently, frequency content. These envelopes can easily be built by an interpolation process of the local extremes, maximums and minimums, from the original time sequence. Similar to modulator signals, they contain more detailed and useful information about the required electrical fault frequencies.

Results show the efficiency of the proposed algorithm and its relevance to detecting and diagnosing faults in induction motors with the advantage of being a technique that is easy to implement in any computational code.

A laboratory investigation carried out through an experimental setup for the study of faults, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance, is presented.

The main contribution of the work is the presentation of an alternative tool to demodulate signals which may be used in real applications like the detection of faults in three-phase induction machines.

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.