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The contribution of rotor bar fault diagnosis and classification in low-speed induction motor drives under varying load torques has been a topic of limited discussion in the literature. Within this respect, the present paper will be discussing this condition. To ensure control performance and investigate the diagnosis process in low-speed operations, the backstepping algorithm is utilized to handle uncertainties. The discrete wavelet transform is employed to detect faults and decompose signals at various levels, while the fuzzy logic algorithm is applied to classify the fault severity. This study's novelty lies in evaluating fault severity for no/low-load conditions by using the energy approximation obtained from the discrete wavelet transforms of the speed regulator's output signal. This approximation is then used as input for the fuzzy fault classification algorithm. The control algorithm and fault diagnosis are validated experimentally using MATLAB/Simulink with a real-time interface based on dSpace 1104 implementation. The results obtained from this procedure demonstrate successful fault detection and severity classification using both the discrete wavelet approximation and its energy eigenvalue. [ABSTRACT FROM AUTHOR]

This work presents a new Fault Tolerant Control approach for a doubly fed induction generator using Iterative Learning Control when the fault occurs. The goal of this research is to apply the proposed ILC controller in conjunction with vector control for doubly fed induction generator to enhance its reliability and availability under broken rotor bars. However, the performances of classical VC control are often characterized by their inability to deal with the effects of faults. To overcome these drawbacks, a combination of VC control and iterative learning control is described. The input control signal of the VC controller is gradually regulated by the ILC harmonic compensator in order to eliminate the faults effect. The improvement of this approach related to active and reactive power ripples overshoot and response time have been explained. Which active and reactive power response time have been reduced more than 84% and 87.5 % respectively. The active and reactive power overshoots have been reduced about 45% and 35% respectively. The obtained results emphasize the efficiency and the ability of the proposed FTC to enhance the power quality in faulty condition. [ABSTRACT FROM AUTHOR]

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time. [ABSTRACT FROM AUTHOR]

Due to their starting and running torque needs as well as their four-quadrant operation, modern industrial drives utilise induction motors (IM). Failures in the rotor bars of the motor can be found using the voltages and currents of each of the three phases as well as the acceleration and velocity signals. For the diagnosis of the quantity of broken rotor bars for a failed IM, conventional signal processing-based feature extraction techniques and machine learning algorithms have been applied in the past. The number of broken rotor bars is determined in this study by looking into a novel technique. For the aforementioned aims, specifically, the deep learning methodologies are studied. In order to do this, convolutional neural network (CNN) transfer learning algorithms are described. Initially, a bandpass filter is used for denoising, and then the signals are transformed using the continuous wavelet transform to create time-frequency pictures (CWT). The collected images are used for deep feature extraction and classification using the support vector machine (SVM) classifier, as well as for fine-tuning the pre-trained ResNet18 model. Metrics for performance evaluation employ categorization accuracy. Additionally, the results demonstrate that the deep features that are recovered from the mechanical vibration signal and current signal yield the greatest accuracy score of 100%. Nonetheless, a performance comparison with the publicly available techniques is also done. The comparisons also demonstrate that the proposed strategy outperforms the compared methods in terms of accuracy scores. [ABSTRACT FROM AUTHOR]

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Summary: The present paper proposes a detection method of the broken rotor bar fault in an induction motor at low‐speed operation. The diagnosis method is based at the first place, on Hilbert Transform (HT) that is used to extract the stator current envelope; then on Discrete Wavelet Transform (DWT) which processes the previously produced signal. As such, the calculation of the stored energy on envelope levels allows to determine the severity of the fault. In this work, the induction motor is controlled at the very low‐speed range and rated load via using sensorless Backstepping control. This nonlinear control is executed to preserve a satisfactory performance speed control during the presence of broken rotor bars to ensure operational continuity. Moreover, Model Reference Adaptive System (MRAS) is used for speed reconstruction to improve the control's system reliability and to reduce its cost. Considerably, through the use of simulation and real‐time implementation using MATLAB/Simulink with the dSpace 1104 control board, the effectiveness of the diagnosis and control techniques is evaluated. [ABSTRACT FROM AUTHOR]

Deep learning-based fault detection approach for squirrel cage induction motors (SCIMs) fault detection can provide a reliable solution to the industries. This paper encapsulates the idea of transfer learning-based knowledge transfer approach and deep convolutional neural network (dCNN) to develop a novel fault detection framework for multiple and simultaneous fault detection in SCIM. In comparison with the existing techniques, transfer learning-based deep CNN (TL-dCNN) method facilitates faster training and higher accuracy. The current signals acquired with the help of hall sensors and converted to an image for input to the TL-dCNN model. This approach provides autonomous learning of features and decision-making with minimum human intervention. The developed method is also compared to the existing state-of-the-art techniques, and it outperforms them and has an accuracy of 99.40%. The dataset for the TL-dCNN model is generated from the experimental setup and programming is done in python with the help of Keras and TensorFlow packages. [ABSTRACT FROM AUTHOR]

It is important to detect the faults of induction motors in an accurate manner due to their widespread use in industrial production processes. Broken rotor bars and eccentricity faults are the common faults of induction motor. The simultaneous occurrence of these two types of faults is often encountered in industrial facilities. Therefore, it is important to properly detect characteristic harmonic components of these types of faults. It is difficult to detect these components at low loading and noisy conditions. In this paper, the simultaneous broken rotor bars and static eccentricity faults are investigated based on the stator current and vibration signals. Hilbert envelope analysis was applied to the stator current and vibration signals under four different loading levels (25%, 50%, 75% and 100%) of induction motor. The frequencies and amplitudes of characteristic harmonic components have been successfully detected in envelope spectrum. The results show that the proposed method can be used to diagnose the simultaneous broken rotor bars and eccentricity faults of induction motors. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for detecting broken rotor bars in squirrel-cage induction motors, using a time-domain current analysis. More particularly, this solution proposes a new use of histogram of oriented gradients since this method is usually applied in computer vision and image processing applications. Fully broken rotor bars have been detected when the motor was running at a very low slip since this operational condition is very difficult to identify using the traditional motor-current signature analysis. In addition, only one phase of the stator current of the machine was applied to extract the intensity gradients and edge directions of each current time window, for both healthy and damaged rotors. It is important to highlight that the present method does not require the slip measurement for fault detection, as demand for other techniques and often related to false negative indications. The features extracted from the histograms have been applied as inputs for a neural network classifier. This method has been validated using some experimental data from a 7.5-kW squirrel-cage induction machine running at distinct load levels (slip conditions) and also for oscillatory loads. [ABSTRACT FROM AUTHOR]

Abstract—At present, one of the most effective methods for detecting dangling bars in squirrel-cage rotor windings of induction motors operating under severe starting conditions is the spectral analysis of stator current in a steady-state operation mode. The existing methods of testing in starting modes are not sensitive enough or their study has not been completed, although it is precisely such methods that allow diagnostics of machines that cannot be inspected in the steady-state operation mode. The purpose of this article is to develop a method for monitoring the state of rotor windings in induction motors operating under severe starting conditions based on the stator current in the starting mode, which is an urgent task. The studies were carried out both on digital models of an induction motor, developed in the Ansys software package, and on an experimental stand with a low voltage supplied to a low-voltage motor to increase the duration of its start. Signal processing is performed in the Matlab software package using the short-time Fourier transform. It is shown that only in the presence of a broken rotor bar, the amplitude of the harmonic component of the order coinciding with the number of motor pole pairs sharply increases at the lower side frequency; this indicates the presence of damage. It is shown that the developed technique for monitoring the condition of rotor windings in induction motors operating under severe starting conditions is more sensitive to broken rotor bars than the existing methods; this makes it possible to timely detect damage to rotor windings and prevent sudden failures of induction motors. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for detecting broken rotor bars in squirrel-cage induction motors, using a time-domain current analysis. More particularly, this solution proposes a new use of histogram of oriented gradients since this method is usually applied in computer vision and image processing applications. Fully broken rotor bars have been detected when the motor was running at a very low slip since this operational condition is very difficult to identify using the traditional motor-current signature analysis. In addition, only one phase of the stator current of the machine was applied to extract the intensity gradients and edge directions of each current time window, for both healthy and damaged rotors. It is important to highlight that the present method does not require the slip measurement for fault detection, as demand for other techniques and often related to false negative indications. The features extracted from the histograms have been applied as inputs for a neural network classifier. This method has been validated using some experimental data from a 7.5-kW squirrel-cage induction machine running at distinct load levels (slip conditions) and also for oscillatory loads. [ABSTRACT FROM AUTHOR]

In this paper a Passive Fault Tolerant Control (PFTC) based on non-linear backstepping control is proposed for a Double Star Induction Machine (DSIM) under Broken Rotor Bars (BRB) fault of a squirrel-cage in order to improve its reliability and availability. The proposed PFTC is able to maintain acceptable performance in the event of BRB. This control technique guarantees robustness against uncertainties and external disturbances and is also able to deal directly with faults by compensating for the effects of the BRB fault in the machine without prior knowledge on the fault, its location and its severity. The stability of the closed-loop is verified by the exploitation of the Lyapunov theory. a comparative study is made between the proposed Fault Tolerance Control (FTC) and Sliding Mode Control (SMC) for demonstrating the performance and effectiveness of the proposed controller. The results obtained show that the proposed FTC has a better robustness against the BRB fault where the DSIM operates with acceptable performance in both speed and torque. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for simultaneous state estimation and broken rotor bars (BRBs) detection in induction motors. The proposed method is named square-root transformed cubature quadrature Kalman filter (SR-TCQKF). The accuracy of the filter has been improved by a transformation on the filter's sigma points, whereas the filter becomes more stable due to the use of the SR version of the covariance matrix. Accordingly, the SR-TCQKF is more accurate and stable than the standard cubature quadrature Kalman filter. The efficiency of the SR-TCQKF has been evaluated for speed estimation and BRB detection in induction motors. The attained results show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The induction motor current is nonstationary by nature, and time-scale analysis techniques such as wavelet packet decomposition (WPD) are more suitable for the analysis of the stator current for broken rotor bar detection. But, WPD is very costly in terms of the computational effort when half-band FIR filter banks are used in analysis. The implementation of four-band FIR filter banks in the analysis of a phase current is proposed here to reduce the computational cost. The use of four-band FIR filter banks with FPGA implementation would also provide higher levels of parallel processing capability resulting in further reduction in computational time required for detection of broken rotor bar faults. In WPD, it is also possible to tailor the frequency band size (resolution) so that one frequency band covers all motor fault induced frequencies due to rotor speed variations. Here, the rms value for broken rotor bar fault-related frequency band is compared with the baseline data to detect any broken rotor bar faults. [ABSTRACT FROM AUTHOR]

Motor current signature analysis (MCSA) is a well-proven technique for electrical and mechanical faults detection in induction motors. The appearance of stator current components or increase of magnitude of some components at characteristic frequencies indicates a motor fault condition. In this paper, we propose a new MCSA fault detector based on a matched subspace technique. The proposed detector consists of three steps. First, the influence of the fundamental supply frequency is removed from the current signal using an interference cancellation technique based on oblique projection. Then, the fault-related frequency is estimated from the interference-free signal using the maximum likelihood principle. Finally, the fault detection is performed using a generalized likelihood ratio test. Simulation and experimental results illustrate the effectiveness of the proposed approach for eccentricity fault, bearing faults, and broken rotor bars detection. [ABSTRACT FROM AUTHOR]

This paper presents an improved method for the broken rotor bar detection in a squirrel-cage induction motor (IM). The method is based on the spectral analysis of the transient stator current signal during the counter-current braking (CCB). Contrary to the classical CCB, the proposed method results in the low braking current, which is a small fraction of the rated value and serves as a broken bar detection test signal only. This kind of the broken rotor bar fault diagnosis is independent of loading conditions and can be carried out even for a free shaft motor. The existence of spectral components in the low CCB current signal indicating the faulty conditions is first proven with the generalized theory of symmetrical components. The method is then verified via simulations, using an IM model based on the finite-element analysis and the magnetically coupled multiple circuits approach. Afterward, the experiments are performed, showing a good agreement with both the theoretical prediction and the simulation results, confirming the presence of the fault-induced components in the stator current spectra. [ABSTRACT FROM AUTHOR]

The aim of this paper is the broken rotor bars fault detection for a sensorless vector controlled induction motor drive. The technique of control used is based on the field-oriented control (FOC), where the fault effect in the mechanical speed is compensated in order to ensure the operation continuity of the machine. The broken rotor fault is detected through the rotor resistance estimation using extended kalman filter observer and the fast Fourier transforms for analyzing a several electrical and mechanical quantities (i.e., rotor speed, quadratic current components of control and stator phase current). The spectral analysis is effectuated only if the variation of the rotor resistance estimation is significant. An EKF observer has proven to be an excellent mathematical tool for the fault indicator in the variable speed drive. Moreover, the spectral analysis using FFT transforms can be a useful solution to ensure that the resistance variation indicates to broken rotor bars fault. The effectiveness of the sensorless control and the fault detection techniques are presented in simulation and validated in a real-time implementation using Matlab/Simulink with the real-time interface (RTI) based on dSpace 1104 board. [ABSTRACT FROM AUTHOR]

The Park’s Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park’s Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park’s/Extended Park’s Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable. [ABSTRACT FROM AUTHOR]

This paper investigates a new fault detection method for induction machines diagnosis. The proposed detection method is based on hypothesis testing. The decision is made between two hypotheses: the machine is healthy and the machine is faulty. The generalized likelihood ratio test is used to address this issue with unknown signal and noise parameters. To implement this detector, the unknown parameters are replaced by their estimates. Specifically, four estimations are required, which are model order, frequency, phase, and amplitude estimations. The model order is obtained using the Bayesian information criterion. Total least-squares estimation of signal parameters via rotational invariance techniques is used to estimate frequencies. Then, phases and amplitudes are obtained using the least-squares estimator. The proposed approach performance is assessed using simulation data by plotting the receiver operating characteristic curves. Two faults are considered: bearing and broken rotor bar faults. Experimental tests clearly show the effectiveness of the proposed detector. [ABSTRACT FROM PUBLISHER]

For discussing the faulty running states of asynchronous motor,using abundant motor and measurement model in Matlab/Simulink, based on multi-loop motor model, a simulation modeling of simple broken rotor bars and an unbalanced voltage supply on asynchronous motor’s operating is introduced to analyze stator current, speed and torque of the motor under various running states. At the same time, different faults with different featured portions are analyzed. The faults and portions include that the motor rotor breaks bars, stator current spectrum is needed to analyze, voltage supply on an asynchronous motor is unbalanced, the stator current’s negative sequence component is needed to analyze. The results show that the spectral analysis method can be applied to the fault diagnosis of broken rotor bars, and stator current negative sequence component presence can be applied to diagnose unbalanced voltage supply. [ABSTRACT FROM AUTHOR]

This paper presents a combination of normalized least mean square (NLMS) filter and Hilbert envelope analysis method to detect the broken rotor bars of a squirrel-cage induction motor. Broken rotor bars are one of the most common fault types that may be seen in induction motors. Rotor faults are reflected in the stator current of the motor as side-band harmonics. Most of the previous works utilize motor current signature analysis using spectral methods to determine required features for detecting motor faults. It may be difficult to detect the faults from stator current when the motor is lightly loaded under noisy conditions. In this study, Hilbert envelope analysis method is used along with an NLMS filter to process the stator current of an induction motor to detect the broken rotor bars. The proposed method is verified experimentally (under 25, 50, and 100 % loading conditions). The results of the recommended method are compared with the results of Fast Fourier Transform (FFT) analysis performed on the stator current of the same motor under the same conditions. The results show that the recommended method gives a more accurate solution compared with the results of FFT method. [ABSTRACT FROM AUTHOR]

Due to the relevance and severity of damages caused in induction motors by broken rotor bars (BRBs), the development and application of new detection methods that offer an efficient and reliable diagnosis in terms of processing and performance are still demanding tasks. This paper presents a methodology based on the Synchrosqueezing transform for detection of BRBs during the startup transient. In order to validate the proposal, a synthetic signal with different noise levels and real current signals of an induction motor are analyzed. Three severities of damage are considered: half broken, one broken, and two broken rotor bars. For automatic diagnosis, a threshold-based stage using the Pearson product-moment correlation coefficient is presented. Both synthetic and real experiment results demonstrate the proposal effectiveness. [ABSTRACT FROM AUTHOR]

A comprehensive study of intelligent tools used to classify broken rotor bars in induction motors, which operate with three different types of frequency inverters, is presented. The diagnosis of defective rotor bars is a critical issue for the predictive maintenance of induction motors. A proper classification of these defects in their early stages of evolution is necessary for preventing major machine failures and production downtime. The proposed approach is performed by analysing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate based on machine frequency supply. To assess classification accuracy under the various severity levels of the faults, the performance of four different learning machine techniques is investigated: (i) fuzzy ARTMAP network; (ii) support vector machine (sequential minimal optimisation); (iii) k‐nearest neighbour; and (iv) multilayer perceptron network. Results obtained from 1274 experimental tests are presented in order to validate the study, which considers a wide range of load conditions and operating frequencies. Experimental results presented in this study validate the robustness and efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel method for broken bars fault detection in the case of three-phase induction motors and under different payloads will be presented and experimentally evaluated. In the presented approach, the cases of a partially or full broken rotor bars are being also considered, caused by: (a) drilling 4 mm and 8 mm out of the 17 mm thickness of the same rotor bar and (b) fully drilled (17 mm) one, two and three broken bars. The proposed fault detection method is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters. The system identification procedure is being carried out on the simplified equivalent model of the induction motor, during the steady-state operation (non-fault case), while at the same time the proposed scheme is able to calculate on-line the corresponding safety bounds for the identified variables, based on a priori knowledge of the measuring corrupting noise (worst case encountered). The efficiency, the robustness and the overall performance of the established fault detection scheme is being extensively evaluated in multiple experimental studies and under various time instances of faults and load conditions. [ABSTRACT FROM AUTHOR]

Induction motors (IMs) are commonly used in various industrial applications. A spectrum synch (SS) technique is proposed in this paper for early IM defect detection using electric current signals; fault detection in this paper will focus on defects in rolling element bearings and rotor bars, which together account for more than half of IM imperfections. In bearing fault detection, the proposed SS technique will highlight the peakedness of the fault frequency components distributed over several fault-related local bands. These bands are synchronized to form a fault information spectrum to accentuate fault features. A central kurtosis indicator is proposed to extract representative features from the fault information spectrum and formulates a fault index for incipient IM fault diagnosis. The effectiveness of the developed SS technique is demonstrated on IM with broken rotor bars and IM with damaged bearing. Test results show that the developed SS technique can detect incipient IM faults effectively. [ABSTRACT FROM AUTHOR]

This paper proposes an improved rotor fault detection algorithm. The main objects are: First, ameliorate the reliability of the rotor fault detection task especially under very low load in the presence of industrial frequency inverter. Second, allow even relatively unskilled users to make an automatic, fast and reliable decision on machine health with low requirements. Basing on the concept of multiple signature processing, the proposed algorithm uses Relative Harmonic Indexes (RHI) as a new fault indicator. The RHI are computed starting from many harmonics selected carefully according to their sensitivity to the occurrence of rotor bar faults, this represents its main advantage compared to the classical fault indicators which use only one or two fault signatures leading commonly to erroneous diagnosis especially under low load. The proposed algorithm requires only the stator current signal. For each data acquisition, the algorithm computes, normalises and classifies the RHI then it displays the obtained results in a simple manner. Extensive experimental tests, carried out on a 3 kW squirrel cage induction motor fed from an industrial frequency inverter, confirm the effectiveness of the proposed algorithm in detecting of rotor bar fault under different operation conditions even with very low loads. [ABSTRACT FROM AUTHOR]

This paper presents the use of an invasive method specially applied for broken rotor bars diagnosis in large induction machines, considering the frequency domain analysis of the magnetic flux density variation in a low-cost Hall effect sensor. The proposed method is based on the Fast Fourier transform, or spectral analysis of the resultant magnetic flux density, to contribute to a crack and bar breaks diagnosis on large squirrel cage motors. More particularly, this paper considers the use of a method capable of overcoming some drawbacks from traditional diagnostic techniques, such as motor current signature analysis during operation at very low slip. The results from the computational simulation and some experimental tests are presented and could be considered for rotor failure diagnostics in induction motors. [ABSTRACT FROM AUTHOR]

Broken rotor bars in induction motors can be dependably detected by analyzing the current signatures under sufficient motor load conditions. Detection becomes less dependable under light motor load conditions. There are also cases in which tolerable motor operating conditions generate current signatures similar to those of motors with broken rotor bars. These cases may present security concerns when the detection element is set to trip the motor and to send alarms. In this paper, we aim to achieve the following: show how broken rotor bars cause characteristic current signatures; show how to detect broken rotor bars with a zero-setting protection element, which uses the current signature method; use cases with different motor operating and fault conditions to analyze the performance of the zero-setting broken bar protection element; identify cases when the current signature method is dependable and cases when security is a concern, and present solutions to address security concerns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, two approach signals are used for broken rotor bar fault diagnosis. One is based on the spectrum analysis, such as the fast Fourier transform, which utilizes the steady-state spectral components of the stator quantities. The accuracy of this technique depends on the loading conditions and constant speed of the machine. The second approach is based on the discrete wavelet transform which is considered an ideal tool for this purpose due to its suitability for the analysis of signals, the frequency spectrum of which is variable in time. These two approaches are tested in simulation and validated experimentally. [ABSTRACT FROM AUTHOR]

This paper presents results of the comparison between the use of induction motor current signature, vibration signature, and acoustic signal analysis methods to detect broken rotor bars fault. The results have taken at healthy and faulty conditions at different speeds due to change of load. It is a comparison between three ways to determine the sensitivity for detecting faults and to adopt the best technology in detecting damages in order to increase the reliability of the system. Signals are been analyzed in time and frequency domains. The value of the root mean square of the current signal, the peak value of vibration signal, and the sound pressure level of the machine sound have monitored for healthy and faulty conditions. The laboratory tests show that the results of analyzing the current signature is considered one of the most sensitive to detect the rotor broken bar faults in its early stages¡ while the vibration signal method is less sensitive to detect the fault in the initial stages because of the presence of other components in the vibration signal. But the acoustic method is not reliable method for detecting this type of the faults. This is due to the presence of noise in the environment and fault low order frequencies. [ABSTRACT FROM AUTHOR]

This paper deals with fault diagnosis of induction machines based on the discrete wavelet transform. By using the wavelet decomposition, the information on the health of a system can be extracted from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. In this paper, we will study the problem of broken rotor bars, end-ring segment, and loss of stator phase during operation. [ABSTRACT FROM AUTHOR]

Faults, such as broken rotor bars, in induction motors may be detected by estimating the spectral signature of the stator currents, particularly the sidebands around the supply line frequency. However, the amplitude of the fundamental frequency (50 Hz) is considerably greater than the sideband amplitude. How to demodulate the signature frequency components under the heavy background of fundamental frequency, or how to remove the fundamental frequency, is becoming a key problem in motor current signature analysis. This paper puts forward a novel transform demodulation algorithm to solve the problem. The three-phase currents are transformed to a magnetic-torque (M-T) coordinate using this algorithm. It is found that the signature frequency components are demodulated in the magnetizing and torque-producing currents obtained by the transformation. Thus, the two demodulated M-T currents can be used to extract the enhanced signature frequency components of faults, and the incipient fault detection of induction motors is easy to realize. With both simulated and experimental data of broken rotor bars, it shows that the proposed algorithm can extract more detailed fault signature frequency components and realize the incipient fault detection of induction motors. [ABSTRACT FROM AUTHOR]

This paper studies on the fault analysis of 3-phase cage induction motor with the mixed fault of the static eccentricity and the broken rotor bars at steady state. A mathematical fault model is presented by the coupled magnetic circuit approach. The calculation of winding inductances is derived by means of a winding function theory. The stator current and the torque are obtained by means of the coupled magnetic circuit method and Park's transformation. The characteristics of single fault and mixed fault are analyzed. From the simulation results it is shown that there is its own peculiar frequency component obtained by the spectrum analysis of the stator current in the case of a single fault. But in the case of mixed fault, there is a mutual effect between two faults. From the comparison with FEM results, it is shown that the proposed approach can be simulated to diagnose faults in 3-phase cage induction motor. [ABSTRACT FROM AUTHOR]

In this paper, the influences of broken bars located at different relative positions in an induction motor are presented. In this investigation, a finite-element model of the squirrel-cage induction motor is developed. Both thermal and electromagnetic analysis of the induction motor operating in conditions of healthy and broken bars fault are carried out, and operating performance and thermal fields with two broken rotor bars located at different relative positions are studied. The accuracy of simulation is verified by experimental results derived from a prototype. From the results, some valuable ideas can be proposed to the broken bars' fault diagnosis. [ABSTRACT FROM AUTHOR]

In this article, a novel criterion function is introduced to diagnose the breakage in rotor bars of induction motors. This criterion function facilitates the precise diagnosis of the fault in induction motors under load variation. It uses wavelet transform to process the stator current signal in the faulty induction motors to extract the wavelet coefficients in a specific frequency band. Furthermore, spectrum analysis of the stator current around the fundamental frequency current is used to diagnose the fault non-invasively. It is shown that the amplitudes of the frequency harmonics components (1 ± 2ks)fs are increased due to the load increases. A time-stepping finite element method is used for modeling the faults in induction motors. In this modeling, the effects of the spatial distribution of the stator winding, nonuniform air-gap permeance, geometrical and physical characteristics of different parts of the motor, and, finally, nonlinearity of the core materials are taken into account. The proposed algorithm is applied to the stator current of a healthy and a faulty induction motor. The simulation results are obtained, and their accuracy is verified by the experimental results. [ABSTRACT FROM AUTHOR]

This paper presents the results of an investigation of the effects of broken squirrel-cage bars. In the investigation, a comprehensive time-stepping coupled finite element approach was fully used to compute stator current waveforms, torque, magnetic flux density waveform, the rotor bar currents, and end-ring currents for three cases: no broken bars, one broken bar, and two adjacent broken bars. The iron core loss distributions in the stator are computed, and the harmonic component of air gap flux density is analyzed. This paper also gives the computed rotor parameters and the vector diagram of the rotor current in cages with and without broken bars. From these data, the faulty signatures are extracted. Experimental results derived from a two-pole 1.1 kW induction motor confirm the validity of the proposed method. Furthermore, this method, which could help to develop diagnostics of broken bars and performance evaluation of induction motor, has great potential in future applications. [ABSTRACT FROM AUTHOR]

This paper introduces a novel frequency pattern for diagnosis of broken rotor bars in induction motors during the start-up period. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. The amplitude of the side-band components of the introduced frequency pattern is a suitable index for diagnosing the broken bars and determining the number of faulty bars precisely during start-up. The paper investigates the performance of a faulty motor. Because the number of broken rotor bars and the load are two major parameters affecting the performance of a faulty motor, the frequency spectrum of torque and speed in a motor with different numbers of broken rotor bars and different loads is analyzed. The time-stepping finite-element method (TSFEM) is used to model the motor. This model takes into account the spatial distribution of the stator windings, nonuniform magnetic permeance of the air gap, nonlinear characteristic of the cores, and continuous rotation of the rotor. The paper compares simulation results in steady-state mode with experimental results and shows that the results agree well. [ABSTRACT FROM AUTHOR]

This article models the simultaneous presence of broken rotor bars and static eccentricity (mixed fault) faults in a three-phase squirrel-cage induction motor using the time-stepping finite element method. A stator current signal is then used for the noninvasive diagnosis of mixed fault. The major problem in the analysis of an induction motor under mixed fault is the determination of the number of broken rotor bars and static eccentricity degree. Broken bars cause an asymmetrical rotor cage and influence the amplitude of the harmonic components. On the other hand, these harmonics are normally used to diagnose the static eccentricity degree independently. Also, static eccentricity leads to asymmetrical air gap flux and flux density distribution, which influence the amplitude of the harmonic components. Therefore, it is not easy to discriminate the contribution of each fault in the mixed-fault motor. Spectrum analysis of the stator current around the main component is used to diagnose the mixed fault and to determine the number of broken rotor bars. It is shown that this spectrum cannot be used to precisely detect the static eccentricity. Therefore, the spectrum analysis of current around the principle slot harmonic is used to diagnose the static eccentricity degree in the mixed-fault case. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current. The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed, and its results are physically explained. This paper also shows that the approximation signal of a particular level, which is obtained from the discrete wavelet transform of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in this paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages. [ABSTRACT FROM AUTHOR]

Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research. [ABSTRACT FROM AUTHOR]

This paper develops the foundations of a technique for diagnosis and characterization of effects of broken bars in squirrel-rage induction motors based on the time-stepping coupled finite-element approach. These studies are performed by using the model to compute healthy case, one-broken-bar fault, and two adjacent broken bars fault performance data, which contains stator starting current waveforms, the current density on the bars, the magnetic force distribution on the rotor bars, and the distribution of magnetic field. Meanwhile, the iron core loss distributions on the rotor tooth adjacent to broken bars are computed, and the harmonic component of air-gap flux density is analyzed. From this data, the faulty signatures are extracted. Theoretical approach together with experimental results derived from a two-pole 1.1 kW induction motor confirms the validity of the proposed method. Furthermore, this method, which could help to develop diagnostics of broken bars and performance evaluation of induction motors, has great potential in future applications. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a method for the diagnosis of rotor bar failures in induction machines, based on the analysis of the stator current during the startup using the discrete wavelet transform (DWT). Unlike other approaches, the study of the high-order wavelet signals resulting from the decomposition is the core of the proposed method. After an introduction of the physical and mathematical bases of the method, a description of the proposed approach is given; for this purpose, a numerical model of induction machine is used in such a way that the effects of a bar breakage can clearly be shown, avoiding the influence of other phenomena not related with the fault. Afterward, the new diagnosis method is validated using a set of commercial induction motors. Several experiments are developed under different machine conditions (healthy machine and machine with different levels of failure) and operating conditions (no load, full load, pulsating load, and fluctuating voltage). In each case, the results are compared with those obtained using the classical approach, based on the analysis of the steady-state current using the Fourier transform. Finally, the results are discussed, and some considerations about the influence of the DWT parameters (type of mother wavelet, order of the mother wavelet, sampling rate, or number of levels of the decomposition) over the diagnosis are done. [ABSTRACT FROM AUTHOR]

In this paper, the authors give a new model of squirrel-cage induction motors under stator and rotor faults. First, they study an original model that takes into account the effects of interturn faults resulting in the shorting of one or more circuits of stator-phase winding. They introduce, thus, additional parameters to explain the fault in the three stator phases. Then, they propose a new faulty model dedicated to broken rotor bars detection. The corresponding diagnosis procedure based on parameter estimation of the stator and rotor faulty model is proposed. The estimation technique is performed by taking into account prior in- formation available on the safe system operating in nominal conditions. A special three-phase induction machine has been designed and constructed in order to simulate true faulty experiments. Experimental test results show good agreement and demonstrate the possibility of detection and localization of previous failures. [ABSTRACT FROM AUTHOR]

Previous work on condition monitoring of induction machines has focused on steady-state speed operation. Here, a new concept is introduced based on an analysis of transient machine currents. The technique centers around the extraction and removal of the fundamental component of the current and analyzing the residual current using wavelets. Test results of induction machines operating both as a motor and a generator shows the ability of the algorithm to detect broken rotor bars. [ABSTRACT FROM AUTHOR]

A new algorithm is introduced for motor current signature analysis of induction machines operating during transients. The algorithm is able to extract the amplitude, phase and frequency of a single sinusoid embedded in a nonstationary wave- form. The algorithm is applied to the detection of broken rotor bars in induction machines during startup transients. The fundamental component of current, which varies in amplitude, phase, and frequency, is extracted using the algorithm. The residual current is then analyzed using wavelets for the detection of broken rotor bars. This method of condition monitoring does not require parameters such as speed or number of rotor bars, is not load dependent and can be applied to motors that operate continuously in the transient mode, e.g., wind generators or motor-operated valves. [ABSTRACT FROM AUTHOR]