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This manuscript proposes a hybrid technique for fault detection and classification in a three-phase induction motor (IM). The proposed hybrid technique combines enhanced ladder spherical evaluation (ELSE) and recurrent neural networks (RNN); hence, it is called the ELSE–RNN method. The main goal of the manuscript is to detect and categorize the faults that occur in the IM. The key objective of the proposed method is to enhance the performance of supercapacitor (SC) storage technology and fuzzy-tuned proportional–integral (PI) supervision over conventional control. The main contribution of this paper is developing an effective fault diagnosis method for three-phase IMs. The presence of stator, rotor, winding, and bearing faults is employed using signal processing techniques, such as the Hilbert transform and SIFT. The proposed ELSE–RNN technique is utilized with the end goal of detecting and classifying faults. Here, the proposed ELSE–RNN technique recognizes motors' healthy or unhealthy conditions in many situations to distinguish faults for protection. The proposed ELSE–RNN technique reduces the complexity of detecting and classifying faults with a validated system and increases the system's accuracy. The ELSE–RNN technique is implemented in MATLAB, and its performance is compared to existing techniques. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes open‐phase fault detection (FD) and tolerant control strategies for wye‐connected three‐phase induction machines (WCTPIMs). The FD mechanism is based on the comparison between predicted and actual values of stator currents, and the control approach is derived from the field‐oriented control (FOC). In the proposed fault‐tolerant control (FTC) method, an asymmetrical current transformation is used for stator current components, and the conventional symmetrical voltage transformation is employed for stator voltage components. It is shown that using these transformations, a modified FOC can be employed for the faulty WCTPIM. The suggested scheme is able to control WCTPIM drives under normal and single open‐phase fault (SOPF) modes with minor adjustments. Experimental results achieved from a laboratory prototype confirm the possibility and good performance of the proposed open‐phase FD and tolerant control for the WCTPIM under different operating conditions. [ABSTRACT FROM AUTHOR]

Summary This article narrates a robust sensor‐less indirect rotor field‐oriented control (IRFOC) of a six‐phase asymmetrical induction motor (SPAIM) drive with an online estimation of parameters and reduction of common mode voltage (CMV). In comparison to the conventional IRFOC technique, the proposed IRFOC provides the least CMV using modified space vector modulation (SVM). To guarantee proper operation of IRFOC, accurate estimation of rotor time constant is mandatory; any mismatch in the actual and tuned value may lead to poor performance of the IRFOC algorithm. This problem will be further accelerated with encoder‐less control using model‐based estimators. In this manuscript, the problem of simultaneous estimation of speed and machine parameters is investigated, and a model reference adaptive system (MRAS) based speed estimator accompanied by online estimation of parameters is proposed to improve sturdiness against variation of parameters. A Simulink model of the proposed method is developed and a simulation study is carried out. To validate the simulation results, a scaled prototype model of The SPAIM rated for 2 HP, 200 V is developed and encoder‐less IRFOC for the SPAIM drives is implemented using an STM32F407VG controller board. The performance of the proposed observers is tested and verified for all the possible operating conditions and results are presented. [ABSTRACT FROM AUTHOR]

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance. [ABSTRACT FROM AUTHOR]

This paper proposes a fault-tolerant control (FTC) strategy using the current space vectors to diagnose sensor failures and enhance the sustained operation of a field-oriented (FO) controlled induction motor drive (IMD). Three space vectors are established for the sensor fault diagnosis technique, including one converted from the measured currents and the other two calculated from the current estimation technique, respectively, measured and with reference speeds. A mixed mathematical model using three space vectors and their components is proposed to accurately determine the fault condition of each sensor in the motor drive. After determining the operating status of each sensor, if the sensor signal is in good condition, the feedback signal to the controller will be the measured signal; otherwise, the estimated signal will be used instead of the failed signal. Failure states of the various sensors were simulated to check the effectiveness of the proposed technique in the Matlab/Simulink environment. The simulation results are positive: the IMD system applying the proposed FTC technique accurately detected the failed sensor and maintained stability during the operation. [ABSTRACT FROM AUTHOR]

This paper introduces a nonlinear PI controller for improved speed regulation in permanent magnet direct current (PMDC) motor drive systems. The nonlinearity comes from the exponential (Exp) block placed in front of the classical PI controller, which uses a tunable exponential function to map the speed error nonlinearly. Such a configuration has not been studied till now, thus meriting further investigation. We consider an exponential PI (EXP-PI) controller and to attain the best performance from this controller, its parameters are optimized offline using salp swarm algorithm (SSA), which borrows its inspiration from the way of forage and navigation of salps living in deep oceans. To indicate the credibility of SSA tuned EXP-PI controller convincingly, numerous experiments on speed regulation in PMDC motor have been implemented using DSP of TMS320F28335. The results obtained are also compared to similar results in the literature. It is shown that the proposed approach performs well in practice by ensuring tight tracking of the speed reference and superb torque disturbance rejection for the closed loop control. Furthermore, superior performance is achieved by the proposed nonlinear PI controller with respect to a fixed-gain PI controller. [ABSTRACT FROM AUTHOR]

Electric-field energy harvesters (EFEHs) have emerged as a promising technology for harnessing the electric field surrounding energized environments. Current research indicates that EFEHs are closely associated with Tribo-Electric Nano-Generators (TENGs). However, the performance of TENGs in energized environments remains unclear. This work aims to evaluate the performance of TENGs in electric-field energy harvesting applications. For this purpose, TENGs of different sizes, operating in single-electrode mode were conceptualized, assembled, and experimentally tested. Each TENG was mounted on a 1.5 HP single-phase induction motor, operating at nominal parameters of 8 A, 230 V, and 50 Hz. In addition, the contact layer was mounted on a linear motor to control kinematic stimuli. The TENGs successfully induced electric fields and provided satisfactory performance to collect electrostatic charges in fairly variable electric fields. Experimental findings disclosed an approximate increase in energy collection ranging from 1.51% to 10.49% when utilizing TENGs compared to simple EFEHs. The observed correlation between power density and electric field highlights TENGs as a more efficient energy source in electrified environments compared to EFEHs, thereby contributing to the ongoing research objectives of the authors. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI). Design/methodology/approach: The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control. Findings: The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller. Originality/value: The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions. [ABSTRACT FROM AUTHOR]

Currently, power trains based on an Open-End Winding Induction Machine fed by a Dual Inverter (DI-OEWIM) are attracting a great deal of interest in various modern industrial applications. However, applying symmetrical control to this system (DI-OEWIM), which is symmetrical in nature, will lead to malfunction. Therefore, the objective of this paper is to explore the influence of asymmetric control on the performance of this system. The principle of this study is to create an asymmetrical control by integrating a phase-shift angle in the Space Vector Pulse Width Modulation (SVPWM) strategy. We then evaluate the impact of these angles on various performances, such as the Total Harmonic Distortion (THD), power losses, Common Mode Voltage (CMV), Zero-Sequence Voltage (ZSV), rotation speed and torque ripple of this system. This study was carried out in the Matlab/Simulink environment and was validated experimentally using the dSPACE 1104 board. The results show that the different angles have significant effects on the overall performance of this system. [ABSTRACT FROM AUTHOR]

Electrical equipment plays a vital role in industry. Among various electrical equipment, induction motors are quite commonly used in many industrial applications. One of the most common faults that occurs in induction motors is bearing fault. In this article, bearing fault is diagnosed in an induction motor using vibration signals with the help of a simple Artificial Intelligence (AI)-based model. Because, the vibration signals are not dependent on the motor type, simple to measure, cost effective and yields good results. In the proposed system, accurate prediction of bearing condition is carried out using Bayesian optimization-based ensemble classifier (BOEC). The performance of the BOEC-based bearing fault diagnosis system is compared with other conventional techniques and the comparison results confirm the superior performance of the proposed system. Also, the accuracy obtained from the BOEC-based bearing fault diagnosis system is 99.97%. To verify the effectiveness of the proposed system, a hardware prototype is set up in the laboratory and bearing conditions of various induction motors are analyzed. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines. Design/methodology/approach: The experimental study involves inducing short-circuit winding turn variations on the induction machine's stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity. Findings: The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults. Research limitations/implications: While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis. Practical implications: The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies. Originality/value: This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation. [ABSTRACT FROM AUTHOR]

When performing bearing fault measurements, the unbalanced magnetic pull's (UMP) influence is inadvertently incorporated. The UMP's influence distorts the measurements used for bearing fault size estimation, leading to inaccurate fault interpretations. In this paper, we combined a magnetic equivalent circuit motor model with a dynamic bearing model to isolate the UMP's effect, which is unprecedented in the state of the art. The coupled model is used to analyse the dynamic interaction between a mechanical fault and the UMP without neglecting the influence of the rotor slot harmonics nor the magnetic potential losses in the stator and rotor. We compare simulations performed with the UMP and without the UMP in the presence of (i) an outer race bearing fault and (ii) an outer race bearing fault combined with an intrinsic rotor misalignment (IRM). The study finds that the UMP amplifies the rotor eccentricity by a non-negligible amount without accounting for an IRM. Moreover, with the inclusion of an IRM, the rotor vibrations are distorted to the point where bearing fault estimation algorithms could become inoperable. The presence of rotor slot harmonics in the UMP has been identified as a key contributor to the distortion. The findings presented in this article can be used to improve bearing fault size estimation algorithms by isolating the disturbances caused by the UMP. [ABSTRACT FROM AUTHOR]

This paper focuses on analyzing asymmetric and open-phase fault modes in symmetrical two-channel six-phase induction machines while considering the spatial harmonics of the electromagnetic field in the air gap. Under sinusoidal power supply, the magnetomotive force exhibits high-order spatial harmonics, which arise due to the winding design. The interaction between these spatial harmonics and the first-time harmonic of the power supply results in the presence of high-order harmonics in the current and electromagnetic torque. The harmonic content of the currents and torque under asymmetric and open-phase operation mode while taking into account spatial harmonics is analyzed. It is shown that in asymmetrical modes, in addition to the 5th, 7th, 11th, and 13th higher harmonics found in symmetrical modes, harmonics in multiples of the 3rd are introduced into the stator winding currents. As for the composition of the electromagnetic torque in asymmetrical modes, all even harmonics are present, in addition to the 6th and 12th harmonics that are characteristic of symmetrical modes. A mathematical model of the six-phase induction machine has been developed using the average voltages within the integration step method. Its adequacy was verified by comparing the simulation results with the experimental results obtained from the developed prototype. [ABSTRACT FROM AUTHOR]

This paper presents a voltage control system for multiphase squirrel-cage induction generators operating at a high variability of speed and variable load. Field-oriented vector control was used with a change in the sequence of the stator phase currents what changes the number of poles of the magnetic field produced by nine-phase stator winding. At low speeds, the current sequence is changed so that the number of poles increases allowing for the desired voltage to be obtained with greater efficiency. The task of the automatic control system was to control the DC voltage to a desired value at the output of the multiphase PWM converter. This is an alternative control method to the scalar control of voltage and frequency presented in a previous work. The control method and parameters of the automatic control system result from the mathematical model of the multiphase induction machine. The results of the laboratory tests were compared with the effects of the operation of the same nine-phase scalar controlled generator. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is introduced in systems with electrical machines, such as in electric drive systems, wind energy generating systems, and small and special machines, to remote monitor and control the operation for data acquisition and analysis. These systems can integrate with the equipment and retrofit the existing installations. At the end of the control loops there are always motors, or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion. This article analyses selected aspects of research and applications of IoT-based control in electric drive systems and of wind energy conversion systems with induction machines. Various applications and study cases of control systems of electrical machines with IoT technology are described. With the IoT-based control of induction machine systems operators can remotely monitor parameters and obtain accurate real-time feedback during fast changing duty cycle operation. Thus, IoT creates multipurpose instruments in the remote control of induction machines. The paper offers a comprehensive analysis of IoT-based control applications in the field of induction machines, with technical details of design, construction, experimental testing, and prototyping, that are useful to energy engineering specialists in the sector of electric drives and wind energy conversion systems. [ABSTRACT FROM AUTHOR]

Predictive maintenance is required for Induction Motor (IM) to avoid sudden breakdown. In this paper, multimodal sensor data are used for prediction of the rotor slot width variation during runtime of IM. Moreover sensor data are visualized through visual image obtained using Scale Invariant Feature Transform (SIFT) and matching dictionary for various faults in motor such as overload, high speed, Broken Rotor bar (BRB), and unbalance magnetic pull. The multimodal sensor signals data acquired from different parts of three phase induction motor are analysed using various transforms such as Over Complete Rational Dilation Wavelet Transform (ORaDWT), Tuneable Q Wavelet Transform (TQWT), Polynomial Chirplet Transform (PCT) and Dyadic Wavelet Transform (Dyadic WT) for various fault conditions and rotor slot width mentioned during runtime condition of motor. The rotor slot width is predicted using Multiple Linear Regression (MLR), Polynomial Regression (PR), Logistic Regression (LR) and Soft-max Regression (SR) methods through the mean value and energy band of the acquired sensor signal. The Dyadic WT and SR perform better for rotor width prediction. The proposed method such as Dyadic WT and SR provides the prediction accuracy of about 95.2%. From experimental results the rotor slot width expansion more than 2% needs immediate attention to avoid breakdown of motor. [ABSTRACT FROM AUTHOR]

The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque. [ABSTRACT FROM AUTHOR]

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications. [ABSTRACT FROM AUTHOR]

Owing to the induction machine's widespread use across most industries, engine failure will be quite expensive. To address this problem, numerous signal processing techniques have been adopted. This work proposed a novel technique called GLTP dependent on local ternary patterns for texture analysis to identify defects in induction motor with the grey level co-occurrence matrix (GLCM). This technique is compared with another original technique named GLKTP based on local ternary pattern using Kirsch operators used to show eight main directional changes in the image and combined with the GLCM matrix for texture analysis. A set of acoustic data is employed with different engine failures (bearing defects and broken bars). The multiclass MCSVM (Support Victor Machine One vs. All) and K-NN (K-nearest neighbourhood) and ANN artificial neural network classifiers are utilized for fault identification. [ABSTRACT FROM AUTHOR]

Ball bearings fault detection with the aid of ultrasound technique is of great importance. Hence, this paper evaluates signal processing of ultrasounds sent by faults and addresses relevant challenges. In this regard, the waveforms of ultrasound signals were analyzed for different types of faults and considering specification of ultrasound channels to enhance the perception of ultrasound signal processing. For this purpose, different conditions have been considered such as waveform fading and the likelihood of frequency fading at lower signal-to-noise ratios. Furthermore, the sensitivity of ultrasonic sensors to fault detection at higher frequencies and the probability of the modulation have been assessed, and modeling results have been presented. In addition to modeling, fault detection in a ball bearing of an electromotor has been studied through an industrial-empirical case study. Therefore, the bearing signals have been processed for fault-free, initial fault, and advanced fault conditions. The studied fault emerged in several months (without hand intervention) and was then intensified over time. According to results, the envelope technique is capable of extracting fault frequencies of ball bearings. Also, high-pass filters have been employed to demonstrate signal shape mode and impulse generation in detail. Moreover, it has been shown that the growth of initial faults can generate new frequencies and fault frequency harmonics other than the fault frequencies of ball bearings. Also, important tips on the ultrasound fault detection of electromotor were provided with a potential effect on the entire fault detection process. These tips came from the author's experiences obtained by monitoring rotary machines. In which, they can serve as a beneficial tool for monitoring of such machines in industrial sites. [ABSTRACT FROM AUTHOR]

In this paper, the performance of different continuous-time and discrete-time models of the electrical subsystem of induction machines and permanent-magnet synchronous machines as well as methods based on them for decoupling the direct and quadrature axis components of the stator current are investigated and compared. The focus here is on inverter-fed, pulse width modulated drives when operated with a relatively large product of stator frequency and sampling time, where significant differences between the models and decoupling methods used come to light. Recommendations for a discrete-time model to be used uniformly in the future are made, as well as statements on whether feedforward or feedback decoupling structures are better suited and whether state controllers improve decoupling measures for very steep speed ramps. Simulation studies and measurement results support the statements made above. [ABSTRACT FROM AUTHOR]

In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models' performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature. [ABSTRACT FROM AUTHOR]

The exploration of electromagnetic vibration sources is very important for the vibroacoustic reduction of a squirrel‐cage induction motor. However, the influence of high‐order electromagnetic force on the electromagnetic vibration of induction motor has not been studied before. The influence of high‐order electromagnetic force on motor vibration is investigated in detail. Firstly, the Maxwell stress tensor method is applied to calculate the electromagnetic force. Next, the force transfer function is introduced to describe the phenomenon that the high‐order electromagnetic force induces the low‐mode motor vibration. Then, the high‐order forces in the air gap of the induction motor are analysed in detail and their effects on motor vibration are simulated. Finally, an experiment is conducted to verify the correctness of the analytical scheme. [ABSTRACT FROM AUTHOR]

Motor current signature analysis is a useful method for detecting incipient faults in induction motors (IMs). However, the harmonic components introduced in the IM current by the local defects of the rolling-element bearing are usually too weak to be detected using common signal processing techniques such as the fast Fourier transform. In this paper, a three-phase IM is considered and local defects are created on its inner ring, outer ring, ball and cage, in turn. Then a proper test rig is prepared to sample and record the stator current at no load, full load and different temperatures under the healthy and defective bearing conditions. The recorded current signals are then decomposed into their intrinsic mode functions (IMFs) using the empirical mode decomposition algorithm. By applying Hilbert transform to the attained IMFs, it becomes clear that the fault-related harmonics are easily detectable in the first IMF. The study reveals that this method enables to detect the bearing local defects on its different parts with different severities under variable operating loads and temperatures. [ABSTRACT FROM AUTHOR]

Today, with the developing new technologies, in addition to the traditional usage areas of asynchronous machines, their usage areas as motors and generators are gradually expanding. In recent years, the use of induction generator in areas such as motor/generator in electric vehicles, wind turbines and microhydroelectric generation has started to widespread. This study aims to design a low-speed and high-torque outer rotor induction machine that can be used in outer rotor induction motor applications and optionally as a direct-drive generator. The design, optimization and electromagnetic analysis of a 16-pole, 50 Hz frequency, 375 rpm synchronous speed, 1 kW outer rotor induction machine were carried out and six different models were developed for the analyses in this study. One slot type and 72 slot were used in the stator designs, 59 slot and three different slot types were used in the rotor design. Copper and Aluminum materials were preferred for the squirrel cage rotor and the study was carried out with the Ansys Maxwell electromagnetic package program. In the study, machine foundation sizes, air gap and slot dimensions were optimized to obtain the highest efficiency using the Rmxprt-optimetrics module. Afterwards, electromagnetic analyzes were performed using the Finite Element Method. As a result of the analyzes made for the motor operating area of the machine, an efficiency higher than the efficiency of 8-pole inner rotor asynchronous motors with 1.1 kW power in IE2 and IE3 classes according to IEC 60034-30-1 was obtained. According to this result, it was found that an outer rotor, low speed and high torque induction machine can be produced and used as a motor. [ABSTRACT FROM AUTHOR]

This article addresses the problem of fault detection in robot manipulator systems. In the production field, online detection and prevention of unexpected robot stops avoids disruption to the entire manufacturing line. A number of researchers have proposed fault diagnosis architectures for electrical systems such as induction motor, DC motor, etc..., utilising the technique of discrete wavelet transform. The results obtained from the use of this technique in the field of diagnosis are very encouraging. Inspired by previous work, The objective of this paper is to present a methodology that enables accurate fault detection in the actuator of a two-degree of freedom robot arm to avoid system performance degradation. A partial reduction in joint torque constitutes the actuator fault, resulting in a deviation from the desired end-effector motion. The actuator fault detection is carried out by analysing the torques signals using the wavelet transform. The stored energy at each level of the transform contains information which can be used as a fault indicator. A Matlab/Simulink simulation of the manipulator robot demonstrates the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Induction machines are the working horses in a lot of industries. Inter‐turn short‐circuit (ITSC) fault is one of the most common failure modes taking place in them. It can generate large fault currents that lead to a local temperature rise in the faulty stator slot, which deteriorates the working performance of the machine or even cascades to a complete machine breakdown. This article focuses on developing transient thermal models for an induction machine under ITSC faults. The aim of these thermal models is to understand the thermal behaviour of the induction machine at different ITSC fault propagation stages. The first thermal model is based on finite element method (FEM) simulation that includes a physics‐based model for the thermal contact resistance. The second thermal model is a lumped parameter thermal network that models the stator as inter‐connected slot‐number parts. They are both capable of modelling the characteristic of non‐uniform temperature along the circumferential direction for the induction machine operating under ITSC fault conditions. After area‐weighted averaging the results of the FEM simulation, it is found they are quantitatively consistent, with an average relative error magnitude of 5%, as well. [ABSTRACT FROM AUTHOR]

To reduce pollution and energy consumption, particularly in the automotive industry, energy saving is the main concern, and hence, Electric vehicles (EVs) are getting significantly more attention than vehicles with internal combustion engines (IC engines). Electric motors used in Electric Vehicles (EVs) must have high efficiency for maximum utilization of the energy from the batteries or fuel cells. Also, these motors must be compact, lightweight, less expensive and very easily recycled. Further, to obtain better dynamic performance, various motor control strategies are used to control the speed of the motor. And to have increased reliability, sensorless speed control techniques that offer sufficiently high performance are used. The sensorless speed control techniques are largely divided into three groups: state observer methods, indirect measurement methods and saliency-based methods. Generally, the state observer uses back emf or flux linkage to estimate the speed of the motor. Since the back emf is directly proportional to the rotor speed, at low-speed back emf based method will give poor performance. The current-based Model Reference Adaptive System (MRAS) model is also popular for estimating low speed; however, assessments deteriorate during high performance applications such as EV. This paper presents an artificial neural network (ANN)-deployed sensorless speed control of permanent magnet synchronous motor (PMSM) drive used in EVs. In this paper, the estimation of speed using the current-based MRAS model is discussed and compared with the proposed ANN-based controller, which shows significant improvement in the performance of EV motor drives. The MATLAB simulation and experimental results are presented to validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach using dominant frequency analysis to diagnose inner race faults in rolling ball bearings of three-phase induction motor. The main objective of the proposed scheme is to identify damaged bearings by analyzing their characteristic frequency components within a specific time interval in segmented signal. This work has been carried out on vibration signal data provided by Bearing Center Case Reserve Western University (CWRU), USA. In this study, IR007, IR014 and IR021 bearing defects are analyzed by Frequency Statistical Analysis in MATLAB. Mean and standard deviation of dominant frequencies are computed from the recorded vibration signals after dividing the signal into multiple segments of equal length. It is observed that both frequency mean, and standard deviation have been found to be highly sensitive with variations of motor speed and connected load. Therefore, motor speed is also studied to calculate the statistical parameters. The test results suggest that the proposed scheme provides comprehensive information about fault analysis through vibration data and could potentially aid researchers in fault analysis using the CRWU datasets. Overall, this paper presents a promising approach to diagnose the faults in the inner race of rolling ball bearings using frequency domain analysis and statistical parameters. [ABSTRACT FROM AUTHOR]

This paper introduces robust nonlinear controller strategies for multiphase induction machines, aiming to enhance operational reliability under healthy and faulty conditions, including stator phase and converter leg openings. Due to the induction machine's inherent nonlinearities and parameter variations, a robust control is required. The study evaluates the effectiveness of the sliding mode control with linear feedback and switched gains, the fuzzy proportional integral control, and their combined application in both healthy and faulty modes. The experimental assessment involves a symmetrical six-phase induction machine in generation mode, with comparisons with a classic proportional integral controller for inner current loop regulations. Experimental results show that the fuzzy proportional integral controller presents the best performance by minimizing torque ripples during both healthy and faulty operations. [ABSTRACT FROM AUTHOR]

The application of electrical drives in the control of centrifugal fans and pumps is a well-established area that leads to substantial energy savings. It requires electrical and automation engineers to have some knowledge relevant to drives about fan and pump modelling since ignoring or oversimplifying fan/pump modelling for the intended drive design compromises the required control quality. This paper improves the existing dynamical models of fans and pumps integrated with induction motors via neural network estimation of overall fan/pump efficiency; this estimation is based on the voltage frequency of the driving induction motor, pressure, and flow rate, followed by the separation of the fan's or pump's own efficiency for the motor load torque computation, enhancing the accuracy due to the correct reflection of the power balance. The model is developed with a view to being convenient for control design applications. For the first time for dynamical models, it is verified experimentally, justifying the necessity of the first-order nonlinear differential equation for the flow rate. The validation includes a direct approach based on analysis of transient behaviour caused by a small-step perturbation of the frequency of the motor voltages and an indirect approach based on the introduced concept of the dynamical flow rate and pressure estimators. [ABSTRACT FROM AUTHOR]

This paper presents the results of load capacity calculations for three-phase squirrel-cage induction motors supplied with distorted voltage with rotating harmonics. The calculations were made on the basis of a commonly used model of an induction machine. The difference from many papers is that the parameters of the equivalent circuit of each motor were precisely determined in terms of power losses in the motor. The load capacity of the motors was made dependent on the load power losses in the rotor cage. These losses were determined on the basis of short-circuit measurements of motors, made for frequencies equal to harmonic frequencies. Measurements and calculations were made for low-voltage squirrel-cage motors with rated powers of 4–65 kW and various efficiency classes. Calculations have shown that the calculated derating curves do not match the curves given in IEC 60034-17 and NEMA MG1. The differences are up to 15% for IE1 and IE2 motors and more than 50% for IE3 motors. [ABSTRACT FROM AUTHOR]

Modern electric machines are attracting great interest from the research community as a result of the increasing number of current applications, including electric vehicles and wind power generators, among others. Different machines, power converters, and control technologies are used, and the number of sensors is usually minimized to reduce the total cost of the system. Particularly interesting are the current and speed sensors, which are essential to the normal functioning of the entire system. This work analyzes different calibration techniques of these sensors, using as a case example a five-phase induction motor drive. Experimental results are included to show the influence of calibration techniques on the system analyzed. The obtained results can be extrapolated to any other similar system. [ABSTRACT FROM AUTHOR]

A two‐degrees‐of‐freedom (2DOF) dual‐rotor axial‐flux induction motor (2DOF‐DRAFIM) is proposed with independent movement capability of rotors and its performance is evaluated in detail. First, using the analytical equations, the steady‐state model of the motor is presented and the electrical equivalent circuit is extracted along with an electromagnetic design algorithm for the proposed motor. In the next step, using two‐dimensional and three‐dimensional finite element modelling (FEM), the performance of the proposed motor is investigated. The results of the experimental measurements on the manufactured prototype confirm the validity of the results of theoretical modelling and finite element simulations. The proposed structure provides some unique features. One of the fascinating behaviours of the proposed 2DOF‐DRAFIM is that the higher‐speed rotor produces higher output torque and the lower‐speed rotor has lower torque. In this regard, the 2DOF structure can be considered a potential concept for electric vehicles (EVs) without needing a conventional mechanical differential. [ABSTRACT FROM AUTHOR]

Recently, studies of squirrel-cage induction machines validate the idea that these machines follow the double-cage induction model; however, the manufacturers categorize these machines as single-cage machines. There are many references that use the squirrel-model for squirrel-cage machines, then machine parameters increase from 5 to 7. In the laboratory, parameter determination is performed to estimate 5 parameters; however, 7 parameters have to be determined. Also, the DC test is not an exact test and is used for approximate estimation. Therefore, locked rotor test equations are changed and an overload test is added. After measurements in the laboratory according to the equations of this paper, a MATLAB program is prepared and 7 parameters are determined in a loop until reaching the desired error. This paper demonstrates that laboratory squirrel-cage machines have to be corrected using the 7-parameter model. Furthermore, this paper shows the necessity of new laboratory test equations for squirrel-cage machines. Then a numerical method is used to determine the machine parameters. [ABSTRACT FROM AUTHOR]

This paper examines the impact of harmonics generated by the power semiconductor devices in the rotor circuit of a Softly Rotor Resistance Variation Induction Motor Drive System (SRRVIMDS) operating under voltage asymmetry. The study evaluates the components of rotor currents, electromagnetic torque, and stator winding currents based on a detailed mathematical circuit model configuration. The analysis also investigates the stator winding distortion currents produced by electromagnetic induction in the stator windings due to the harmonic components of the rotor current. The paper provides relationships for estimating all harmonic order frequencies of the stator currents, rotor currents, and torque as a function of the stator frequency and rotor speed. The results show that the investigated currents have sufficient accuracy, except at a slip of one-sixth, where notable differences occur. [ABSTRACT FROM AUTHOR]

Induction motors (IM) are the most frequently used type of motor in the industry. The number of rotor slots, bar geometry, and conductivity of bar material have a strong impact on the torque profile and efficiency characteristics of induction motors. This study focused on investigating the effect of different rotor bar designs on motor performance by the finite element method (FEM). The IMs have been designed using the same stator core, winding, and core lengths. The total rotor bar cross-section areas are also fixed throughout all designs. In addition to the change in the number of rotor bars and geometry, the effect of copper and aluminum bar materials on motor performance was also investigated, both for single and double-layered squirrel-cage structures. The results of the study indicate that the starting torque of the motor in a 36/30-slot aluminum single-cage structure was obtained as 96.26 Nm, while the starting torque of a 36/46-slot aluminum double-cage structure was found to be 115.34 Nm. It is also found that the starting torque of the initial design can be increased by up to 19.82% by changing only the rotor bar numbers and material with the same stator and rotor size. The efficiency of the motors was determined as 86.6% for both designs. In addition to efficiency, the output torque ripple has been decreased to 2.63, which equals a 67.32% decrease in the ripple of the initial design. The improved design has an approximately 8 °C lower T2 due to better cooling performance as a result of a higher number of rotor slots. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase "a" of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs. [ABSTRACT FROM AUTHOR]

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]

The fault tolerance proprieties of multiphase induction machines make them candidate technologies for future electrified transport systems. Indeed, the use of multiphase drives is primarily recommended for their redundancy, which allows them to handle increasing power demands as well as provide fault-tolerant operation. One of the most common failures in polyphase induction machines is open-phase fault (OPF). Under this degraded mode, machine performance deteriorates with increased torque ripples and copper losses. This work investigates the fault-tolerant operation of dual star induction machines (DSIM) with two connected neutral points in which a new post-fault operation technique is introduced to manage the OPFs. The machine was modeled on the natural reference frame, and no transformations were used. The phase opening is caused by inserting a high-value resistor in series with the faulty phase, which cancels the current flowing in the latter. A post-fault reconfiguration strategy that reorganizes the power converter in order to cancel torque ripples by exploiting the principle of multi-biphase modeling of the DSIM is proposed. The performance of the proposed reconfiguration strategy was verified through detailed simulation results. [ABSTRACT FROM AUTHOR]

An SMR (small modular reactor) is expected to be able to operate flexibly in conjunction with a high renewable energy penetration grid as a result of improved safety and easy power control compared to large nuclear power plants. SMRs, such as South Korea's System-integrated Modular Advanced ReacTor (SMART), are designed to use canned motors (CMs) for their reactor coolant pumps (RCPs) to enhance their safety. CMs passively enhance the reactor's safety by preventing the leakage of radioactive reactor coolant. However, with motor sizes designed to be as small as possible, and the increased air gap of CMs between the stator and rotor, the starting torque may be insufficiently low compared to that of similar-sized induction motors (IMs). Thus, CMs may require variable frequency drives (VFDs) to start. This paper compares the torque characteristics of CMs with those of IMs for SMART's RCPs. ETAP is then used to perform a motor-starting analysis for CMs activated with and without VFDs. The results are presented and analyzed to find out if VFDs can deal with the CM starting torque issue. [ABSTRACT FROM AUTHOR]

With the widespread application of permanent magnet synchronous motor (PMSM) drives, the demand for safety and reliability has increased. Inverter open-circuit fault is a frequent and severe failure that requires reliable detection and remedial measures. This paper proposes an open-circuit fault diagnosis technique based on voltage residual analysis that does not require additional voltage sensors. The proposed technique analyzes the characteristics of the reference voltage and the actual output voltage under both healthy and fault conditions. To achieve fast and reliable fault detection, it uses the DC component and second harmonic component in the residual voltage between estimated voltage and controller reference voltage. Once an open-circuit fault is detected, the polarity of the DC component is capable of identifying the fault location. The effectiveness and robustness of the proposed technique are verified through simulations and testing on a triple redundant PMSM under various load and speed conditions. [ABSTRACT FROM AUTHOR]

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems. [ABSTRACT FROM AUTHOR]

Induction motor fault diagnosis and condition monitoring are on the anvil of researchers, who have reported many studies and developments in the literature. However, the reported technique requires intervention to interpret the frequency and time–frequency domain analysis results. Intelligent systems have been reported to overcome these limitations; however, time delays and additional costs are incurred owing to the need for expert interpretation. Hence, this work proposes a methodology based on motor current signal processing in image format to automatically diagnose and classify the inter-turn fault of the three-phase induction motor. The motor current sampled in the time domain by shortening the inter turns in the winding for different load conditions is expressed as three-color monotonous images for each phase. These obtained images are applied for training and testing of convolution neural network, which can classify the faults according to their severity and identify the phase in which the fault prevails. Furthermore, efforts have been made to design the convolution neural network architecture, i.e., selecting hyperparameters such as learning rate, batch size, number of epochs, and momentum rate, and average classification accuracy of 99.38% is obtained. The proposed technique with monotonous coloured images of current signal as an input to CNN is found to be effective in terms of computational time, robustness to identify the faults even in the presence of noise and fault classification accuracy when compared to other published literature. [ABSTRACT FROM AUTHOR]