Rotor Bars Research Articles

Reduced Order Modeling and Parameter Identification of Induction Motors Based on FEA Solutions

Saturation and skin effects are two key factors which affect the simulation accuracy of three-phase induction motors. This paper introduces a methodology to decouple the rotor parameters to consider the saturation effect and skin effect independently. The skin-effect parameters are further modeled by a two-branch circuit with optimized sectioned-bar parameters. Then, a reduced order model (ROM) is developed based on the two-rotor-branch circuit to consider the skin effect in the time domain. All model parameters are identified by a two-stage algorithm based on FEA solutions in the frequency domain at various stator phase currents and rotor slips, using a locked rotor scheme with equivalent rotor conductivities. The first stage identifies both the decoupled saturation-effect parameters and skin-effect parameters, and the second stage identifies the two-rotor-branch parameters based on the optimal value of the rotor bar section ratio. The simulation results using the proposed model are compared with the transient FEA results and measured data.

An automatic rotor bar fault diagnosis using fuzzy logic and DWT-energy for backstepping control driven induction motor in low-speed operation

An automatic rotor bar fault diagnosis using fuzzy logic and DWT-energy for backstepping control driven induction motor in low-speed operation

Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.

Innovative Conveyor Belt Monitoring via Current Signals

This paper proposes, investigates, and validates, by comprehensive experiments, new online automatic diagnostic technology for belt conveyor systems based on motor current signature analysis (MCSA). Motor current signature analysis (MCSA) is a method employed for detecting faults in electric motors by analyzing the current waveforms generated during motor operation. The technology capitalizes on the fact that motor defects, such as mechanical misalignment, bearing damage, and rotor bar defects, cause variations in a motor’s current waveforms, which can be discerned and analyzed using advanced signal processing techniques. MCSA is a non-invasive and cost-effective technique that can detect motor faults in real-time without requiring expensive equipment or disassembly of the motor. In this study, the researchers tested the proposed diagnostic technology, which relies on a power feature. The power feature is calculated as the integrated power within a specific frequency range, centered around the fundamental harmonic of the supply frequency. The purpose of the study is to evaluate for the first time the effectiveness of the proposed diagnostic technology for the diagnosis of a tracking of a belt conveyor. The proposed technology’s effectiveness is assessed using current signals that are obtained for two different scenarios: the normal belt tracking, and a belt mis-tracking under two different loads of a belt conveyor system. The study’s findings indicate that the proposed technology has a high level of diagnostic effectiveness when used for belt mis-tracking. Therefore, it is feasible to recommend this technology for diagnosing tracking issues in belt conveyors.

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

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.

Modeling and Simulation of Faulty Induction Motor in DQ Reference Frame Using MATLAB/SIMULINK with MATLAB/GUIDE for Educational Purpose

Owing to their robust structure, induction motors are preferred to be used under difficult working conditions. Therefore, various faults may occur in the motor due to unexpected forces during the operations. Obtaining the data through experimental methods by physically creating faults in the induction motors, and analyzing their behavior is not efficient in terms of cost and time for educational purposes. Considering the above negative situation, in this paper, mathematical models have been developed in the dq0 stationary reference frame expressing three-phase stator windings short-circuit fault and broken rotor bar fault in induction motors. The proposed models are faster as compared to the other analytical models in terms of computation due to the rotor position independence of the inductance matrix. The faulty induction motor mathematical models have been implemented in the MATLAB/SIMULINK software environment with detailed explanations of each faulty model’s subsystem. As a visual laboratory that can be used as an educational tool for the analysis of a three-phase faulty induction motor, a graphical user interface application has been developed in MATLAB/GUIDE, which allows users to simulate models from a single interface. As a case study, the behaviors of faulty induction motors in transient and steady states have been simulated in different severity scenarios of the faults. The park vector method has been used as a fault diagnosis approach to investigate fault types and the fault severity effects on the park vector pattern in each fault scenario. In addition, to observe the success of the developed Simulink toolboxes, they were used at Marmara University through courses in electrical machinery and evaluated by the graduated students at the end of the semester.

Neural network based detecting induction motor defects supplied by unbalanced grid

<span lang="EN-US">Predictive diagnosis of motor defects can reduce repair and downtime costs of electrical equipment. This paper presents the results of a research on the effectiveness of the defect classification and recognition system based on convolutional neural network to detect defects in a squirrel-cage induction motor based on the stator supply voltage and output phase currents when supplied from an industrial grid of limited power with possible voltage asymmetry and harmonic distortion taken into account. In this work a simulation model, implemented in MATLAB/Simulink, is proposed to investigate unbalanced conditions. The mathematical model has been verified on a physical test bench and then used to create a database of currents from measured asymmetric grid voltages in the presence of defects such as short circuit between turns of one phase, rotor bar breakage, rotor eccentricity and bearing defects. The classification quality of all defects in the neural network was 72%, with the exclusion of intercoil short circuit defect and the merging of different bearing defects, the quality of the model was achieved 91%. A further increase in the quality of the defect recognition system is due to a building up modification of the neural network architecture.</span>

Derived Functions for MATLAB Non-Linear Solvers: An optimal rotor slot and bar design stopgap

The most striking difference between the Squirrel cage induction motor (SCIM) and other electric motors lies invariably in the rotor. The major running performance characteristics, together with the entire acceleration performance range, are all influenced, in varying degrees, by the rotor slot and bar design. Therefore, proper dimensioning of the slots and bars is therefore critical to attaining a high performing machine. The preliminary stage of the SCIM design, if well done, facilitates the refinement and optimization stages; serving as a good take-off point for ultimately realizing that final state of the art design. This study therefore attempts to derive relevant design functions that could be fed into the algorithms of various non-linear solvers, one of which is the Genetic Algorithm (GA), to output the rotor slot initial main dimensions for the SCIM; from where proper design refinement/optimization could cheaply evolve. The SCIM design obtained from implementing the derived equations, when compared with the appropriate reference datasheets, showed only minor deviations from the expected performance indices – an outcome deemed satisfactory for a preliminary design attempt. The so obtained initial dimensions may in practice be subjected to the appropriate refinement, depending on the target performance requirement of the motor to be designed.

The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines.

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.

A comparative case study between shallow and deep neural networks in induction motor's fault diagnosis

AbstractHere, performance of auto‐encoder deep neural networks in detection and isolation of induction motor states (healthy, bearing outer race fault, stator winding short circuit and broken rotor bar) in the presence of unbalanced power supply and electro‐pump dry running disturbances is investigated. Easily available three‐phase electrical current signals are denoised using independent component analysis, and then the frequency‐domain signal is used to train a neural network. A comparison is made between shallow and deep neural networks and also between the conventional structure of deep methods and the encoder–decoder structure in terms of training and test accuracy and robustness. In fact, the depth is increased and the effectiveness is investigated. At the end, it is shown that an encoder–decoder structure leads to the best result in terms of accuracy and robustness. The algorithms are examined experimentally, and the results show that the auto‐encoder deep neural network can detect the aforementioned faults with a high reliability in the presence of disturbances.

Overview of Fundamental Frequency Sensorless Algorithms for AC Motors: A Unified Perspective

This article uses the active flux concept to review fundamental frequency sensorless algorithms for both induction and permanent magnet motors in one framework. Fundamentally, sensorless torque estimation can be directly solved using voltage-current model (VM) estimator or indirectly solved using current-speed model (CM) estimator. The latter turns the torque estimation problem into a speed estimation problem. The stator flux in VM and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -axis angle in CM are deemed as the two sets of original states for the sensorless drive. Through the change of states, the direct torque estimation can be realized via observer designs, whereas the speed dependency in the dynamics of the unknown state [e.g., active flux and electromotive force (emf)] gives rise to a class of speed estimation methods, known as model reference adaptive system (MRAS). The idea of a general speed observer is proposed to summarize various separate speed estimation methods needed for direct torque estimation. It is suggested to adopt inherently sensorless designs such that two-way coupling between torque estimation and speed estimation is avoided. For induction motors, it turns out that the unmodeled voltage in the active flux dynamics reveals current flowing in rotor bars and can be further modeled, for which the solutions to regeneration instability problem are discussed, and change of states is recommended to attain global stability. Finally discussed are the results of the slow reversal test, where local weak observability of ac motors can be potentially preserved.

A Fitted empirical demodulation for low frequency electrical signal evaluation

Empirical Demodulation (ED) is a technique used to build a discrete signal, called an empirical envelope, from a modulated time sequence. For example, in three-phase induction motors, this envelope can carry fault frequency data that allows the machine health status to be evaluated during a spectral analysis. However, due to mathematical reasons, the method is very sensitive to the amplitude oscillations within the signal. When these oscillations are unwanted, as in the presence of measurement noise, the results can be strongly affected. This work proposes an iterative and adjustable version of the ED that considerably reduces its sensitivity to the presence of high frequency noise, thus eliminating the need for signal pre-filtering. To prove the effectiveness of the Fitted Empirical Demodulation, the authors applied the new tool in motor current signals for analysis of the rotor bars conditions.

A study of the dependence of the 3-phase cage motor maximum torque on the centroid of the rotor bar section

The paper investigates the relative influence of the centroid of the three-phase squirrel cage induction motor rotor bar cross section, on the breakdown (maximum) torque of the machine. The influence of the angle of taper, the top width and the radial depth of the bar section, as established design variables, were also investigated so as to properly situate the degree of influence of the centroid, in comparison; as far as the maximum torque is concerned. The machines were investigated in their steady state operating mode using the equivalent circuit method. The machine learning capabilities of the Least Square Support Vector Machine (LSSVM) was deployed to extract by prediction, information about the likely dependencies between the randomized block of the geometric variables and the maximum torque, and the captured information was stored using the Root Mean Square Error (RMSE) of predictions. The validated results show that the rotor leakage reactance – a part determinant of the maximum torque; tends to show some significant sensitivity to a design change in the centroid of the transverse section of the rotor bar.

A study of the dependence of the 3-phase cage motor maximum torque on the centroid of the rotor bar section

A study of the dependence of the 3-phase cage motor maximum torque on the centroid of the rotor bar section

Influence of squirrel cage induction rotor geometry in battery C-rating

An external rotor induction motor (ERIM) is designed to propel an electric two-wheeler with improved torque to current ratio. The torque of the optimized ERIM is significantly based on the rotor cage. The rotor’s specific permeance varies by varying the bar’s shape, increasing the motor’s torque. Likewise, maintaining the flux density below the saturation state keeps the current low. This research work proposes and studies various rotor bar shapes to improve the torque to current ratio of the motor. The proposed bars are optimized with finite element analysis (for rotor bar shape selection) and a metaheuristic approach (for rotor cage sizing). Improvement of torque to current ratio reduces the discharge C-rating of the battery, being the source for the ERIM structure.

Expert System Based on Autoencoders for Detection of Broken Rotor Bars in Induction Motors Employing Start-Up and Steady-State Regimes

Induction motors are indispensable, robust, and reliable machines for industry; however, as with any machine, they are susceptible to diverse faults. Among the faults that a motor can suffer, broken rotor bars (BRBs) have become one of the most studied ones because the motor under this fault condition can continue operating with apparent normality, yet the fault severity can quickly increase and, consequently, generate the whole collapse of the motor, raising repair costs and the risk to people or other machines around it. This work proposes an expert system to detect BRB early, i.e., half-BRB, 1-BRB, and 2-BRB, from the current signal analysis by considering the following two operating regimes: start-up transient and steady-state. The method can diagnose the BRB condition by using either one regime or both regimes, where the objective is to somehow increase the reliability of the result. Regarding the proposed expert system, it consists of the application of two autoencoders, i.e., one per regime, to diagnose the BRB condition. To automatically separate the regimes of analysis and obtain the envelope of the current signal, the Hilbert transform is applied. Then, the particle swarm optimization method is implemented to compute the separation point of both regimes in the current signal. Once the signal is separated, the two autoencoders and a simple set of if-else rules are employed to automatically determine the BRB condition. The proposed expert system proved to be an effective tool, with 100% accuracy in diagnosing all BRB conditions.

Current Park’s Vector Pattern Technique for Diagnosis of Broken Rotor Bars Fault in Saturated Induction Motor

Current Park’s Vector Pattern Technique for Diagnosis of Broken Rotor Bars Fault in Saturated Induction Motor

Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison

In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time-frequency analysis, frequency analysis and time domain. The results show that this method improves the rotor fault detection in transient conditions. Variable speed drive applications are common in industry. However, traditional condition monitoring methods fail in time-varying conditions or with load oscillations. This method is based on the combined use of the Hilbert and discrete wavelet transforms, which compute the energy in a bandwidth corresponding to the maximum fault signature. Theoretical analysis, numerical simulation and experiments are presented, which confirm the enhanced performance of the proposed method with respect to prior solutions, especially in time-varying conditions. The comparison is based on quantitative analysis that helps in choosing the optimal trade-off between performance and (computational) cost.

A Design Approach to the Analysis of Rotor Slip Dominance in Torque Production during Low Slip Motoring.

The study beams more light on torque production at low slip frequency, where the effect of rotor resistance clearly overrides that of the rotor leakage reactance. It is widely admitted that the cross-sectional area (CSA) of the rotor bar has the major design influence on the rotor resistance (R2 ) at this slip region. Besides, the low slip approximation of the developed torque equation tends to reveal that at a given constant voltage and frequency, the only parameter by which the squirrel cage induction motor (SCIM) designer could significantly influence torque development for a given load torque, is the R2 . But it is also widely agreed that torque is operationally dependent on slip, and that the slip at which a given torque is produced rises with R2 as the slope of the torque speed curve decreases; and this seems to imply the flow of more current at a given operating point. Further, it is also true that this current rise ought to be limited by this increase in R2 from the design or redesign of the rotor bar. There seems to be a subtle conflict between the individual influence of rotor slip and R2 as far as torque is concerned. However, this study shows that the superior rate of change of slip, relative to that of R2 in their respective responses to a design modification in bar CSA, largely supports the fact that slip will always dominate in influence on torque at the high-speed motoring mode of the SCIM.

Motor Fault Diagnostics Based on Current Signatures: A Review

Electric motors act as the backbone of industrial development. Their reliable and safe operation is essential to various industries. At present, motor fault diagnosis based on current signatures has progressively gained favour thanks to their non-invasive nature. This review summarizes recent advances in motor current signature analysis for fault diagnosis. First, motor diagnostic background and requirements are expounded, as well as the benefits of current signature analysis. Then, the mechanism and influence of common faults for the most widely used induction motor and permanent magnet synchronous motor are analyzed, and the detection criteria for typical motor defects, such as bearing fault, stator windings inter turn short circuit, and broken rotor bar, are summarized. Next, the motor diagnosis techniques based on current signature analysis are summarized from the technical perspectives of spectrum analysis, demodulation transformation, time-frequency analysis, parameter estimation, artificial intelligence, and etc. Pros and cons of each technology are also provided. Finally, according to the challenges faced by the existing technologies in engineering, future research is suggested to focus on influence analysis of electromechanical coupling on current signal, feature extraction for incipient fault, non-stationary signal analysis, unlabeled data utilization and fault severity assessment.