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![Possible failure modes that can affect the stator windings [10].](publication/347911298/figure/fig1/AS:11431281213016659@1702928311512/Possible-failure-modes-that-can-affect-the-stator-windings-10_Q320.jpg)
![Aging factor of motor insulation for classes A, B, F, and H [15].](publication/347911298/figure/fig3/AS:11431281213016661@1702928311955/Aging-factor-of-motor-insulation-for-classes-A-B-F-and-H-15_Q320.jpg)
Three-phase induction motors are considered to be the workhorse of industry. Therefore,
induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrenc...
Contexts in source publication
Context 1
... this impact depends on the insulation system classes. Table 1 introduces the different categories of the insulation systems according to the NEMA and IEC 60085 standards [15,16]. ...Context 2
... 3 illustrates the percentage of life vs. temperature for the insulation system classes A, B, F, and H. Table 1. Insulation system classes, according to the NEMA and IEC 60085 classification [15,16]. ...Context 3
... [16] Old IEC 60085 Thermal Class [16] NEMA Class [17] NEMA/ UL Letter Class Maximum Hotspot Temperature Allowed [°C] Relative Thermal Endurance Index (°C) [16] 90 Y 90 >90-105 105 A 105 A 105 >105-120 120 E 120 >120-130 130 B 130 B 130 >130-155 155 F 155 F 155 >155-180 180 H 180 H 180 >180-200 200 N 200 >200-220 220 220 R 220 >220-250 S 240 250 250 >250 ...Similar publications
The induction motors are widely used in the industry for their advantage of being reliable, rugged, and simple in construction. Nonetheless, the use of inverter-based drives has increased the overall system failure possibility mainly because of the power electronics switches used in the inverter itself. For that reason, it is crucial to monitor the...
Citations
... [1]. IMs, which potentially establish emphasized conditions, under which their performance is unfavorably affected, are seen in numerous applications [2]. In addition to its benefits, the squirrel cage induction motor (SCIM) has a low beginning torque. ...
Three-phase induction motors (IMs) are generally used in commercial, industrial, and domestic applications due to their advantages, such as good self-starting ability, simplicity, high reliability, cost-effectiveness, low maintenance, and ruggedness in construction. The IMs are exposed to different internal and external faults; one of the most popular external faults is unbalanced supply voltages. Unbalanced supply voltage is a popular and worldwide phenomenon that effectively decreases the characteristics of IMs. The present work shows the adverse effect of unbalanced supply voltages on the steady-state characteristics of all NEMA (National Electrical Manufacturers Association) designs of 20hp squirrel cage IMs (SCIMs). A symmetrical component is used to determine the performance of each NEMA design operating in different unbalanced supply voltage situations. The importance of this paper is that it likens different NEMA designs regarding torque-speed characteristics, efficiency, power factor, stator currents, rotor currents, torque pulsation, a ripple in rotor speed, and starting-up performances when subjected to over and under-unbalanced supply voltage conditions. Also, the MATLAB and Simulink environments have been utilized concurrently for simulation purposes.
... Among these issues, stator winding failure stands out as a significant concern due to its potential to disrupt operations and incur costly repairs. Stator winding failure can occur due to various reasons, including insulation degradation, overheating, mechanical stress, and environmental factors [3]. In general, stator winding faults are classified as short circuits, open circuits, inter-turn faults, and ground faults, and can have significant impacts on industrial operations [4]. ...
... Similarly, the phases Nbs and Ncs have Nbs = Ncs = Ns, and the severity of the fault may be varied by adjusting the number of shorted turns. Assuming the stator winding has a different number of turns, the flux linkage of the stator and rotor for the fault model of SCIM is given in (3). ...
... Several papers analyzing the impact of SVU on the performance of SCIMs can be found in the literature, such as [6][7][8][9][10][11][12][13][14][15][16]. In [17] and [18], the impact of the star (Y) and delta (D) connection modes on the SCIM tolerance to SVU and saturation-related losses is addressed. ...
... The unbalance voltage is defined by The IEEE through the phase voltage unbalance rate (PVUR), which is given by equation (2) Unbalanced supply voltage to the induction motor can degrade motor performance as well as shorten the induction motor's life. A small amount of voltage unbalance can cause large current unbalances in the motor, resulting in temperature rise and, ultimately, insulation failure [28]. ...
This paper presents a computational tool for detecting unbalanced voltage supplies in induction motors. For real-time applications, a technique based on the Fast Fourier Transform (FFT) was used to compute the desired component frequency. A Labview virtual instrument was used to implement the computer algorithm. When supply voltage unbalance appears identical, fault diagnosis using current signature analysis becomes more difficult. The detection percentage is calculated in the following conditions: balanced voltage, under voltage (10%), over voltage (5%), and mixed voltage (UV 5% + OV 5%). The unbalanced voltage waveforms are then analyzed using the classical Fast Fourier Transform (FFT). The results were detected at frequencies of 25 Hz and 75 Hz with a high amplitude of 30 dB, when compared with healthy conditions at this frequency there was no spike in amplitude values. So by using the MCSA method with the fast Fourier transform approach analysis, it was successful in detecting voltage unbalance disturbances in 3-phase induction motors.
... For these reasons, early recognition of abnormalities is important to identify any faults at an incipient stage can help to avoid catastrophic failure and global damage. Literature has reports that electrical faults are principal causes [1][2][3], inter-turn short circuit (ITSC) have a significant share with approximately 30 % to 40 % and broken rotor bars (BRB) which represent 5-10 % of all the IM faults. These faults are caused by several forms of stress such as thermal, electrical, mechanical and environmental. ...
... This model allows detection of short circuit (SC) faults in stator winding and predicts it before it grows and damages the machine completely. In [3], a thermal model analysis of IM relies on finite elements method used to identify how ITSC faults of different severity affect the temperature of the IM. However, this method needs times after starting the motor to estimate the failure severity. ...
... The testing process involves a dataset separate from the one used for training, providing an assessment of the network's ability to generalize to new, unseen data. Figure 9 depicts the test data set of the system under different operating cases of IM: healthy case (7 samples), fault of shorted turns (1,3,5,7,9), and fault for 1 and 2 BRB, are obtained under 7 load torques (0.5, 1.5, 2.5, 3.5, 4.5, 5.5 and 6.5 Nm). The test output of the NN (T1, T2, T3) is accurately equal to (1, 0, 0), (0, 1, 0), and (0, 0, 1). ...
Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine.
... For these reasons, early recognition of abnormalities is important to identify any faults at an incipient stage can help to avoid catastrophic failure and global damage. Literature has reports that electrical faults are principal causes [1][2][3], inter-turn short circuit (ITSC) have a significant share with approximately 30 % to 40 % and broken rotor bars (BRB) which represent 5-10 % of all the IM faults. These faults are caused by several forms of stress such as thermal, electrical, mechanical and environmental. ...
... This model allows detection of short circuit (SC) faults in stator winding and predicts it before it grows and damages the machine completely. In [3], a thermal model analysis of IM relies on finite elements method used to identify how ITSC faults of different severity affect the temperature of the IM. However, this method needs times after starting the motor to estimate the failure severity. ...
... The testing process involves a dataset separate from the one used for training, providing an assessment of the network's ability to generalize to new, unseen data. Figure 9 depicts the test data set of the system under different operating cases of IM: healthy case (7 samples), fault of shorted turns (1,3,5,7,9), and fault for 1 and 2 BRB, are obtained under 7 load torques (0.5, 1.5, 2.5, 3.5, 4.5, 5.5 and 6.5 Nm). The test output of the NN (T1, T2, T3) is accurately equal to (1, 0, 0), (0, 1, 0), and (0, 0, 1). ...
Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.
... The collection of energy is an intriguing potential source of renewable energy. Induction motors that are self-powered can obtain their power from energy harvesting [60]. Engineers can optimize the thermal behavior of these systems to ensure their continued effectiveness and dependability over the long term. ...
In recent times, there has been an increased demand for electric vehicles. In this context, the energy management of the electric motor, which are an important constituent of electric vehicles, plays a pivotal role. A lot of research has been conducted on the optimization of heat flow through electric motors, thus reducing the wastage of energy via heat. Futuristic power sources may increasingly rely on cutting-edge innovations like energy harvesting and self-powered induction motors. In this context, effective thermal management techniques are discussed in this paper. Importance was given to the potential energy losses, hotspots, the influence of overheating on the motor efficiency, different cooling strategies, certain experimental approaches, and power control techniques. Two types of thermal analysis computation methods, namely the lumped-parameter circuit method (LPCM) and the finite element method (FEM), are discussed. Also, this paper reviews different cooling strategies. The experimental research showed that the efficiency was greater by 11% with the copper rotor compared to the aluminum rotor. Each rotor type was reviewed based on the temperature rise and efficiency at higher temperatures. The water-cooling method reduced the working temperatures by 39.49% at the end windings, 41.67% at the side windings, and by a huge margin of 56.95% at the yoke of the induction motor compared to the air-cooling method; hence, the water-cooling method is better. Lastly, modern cooling strategies are proposed to provide an effective thermal management solution for squirrel-cage induction motors.
... In today's industrial landscape, three-phase induction motors (IMs) dominate, accounting for over 85% of all electric motors utilization [1][2][3][4]. Their widespread adoption stems from their reliability, ease of design, high performance, and ability to handle heavy loads, making them suitable for various applications across manufacturing, processing, power systems, transportation, and more. Despite their benefits, IMs operate in challenging mechanical and electrical environments, rendering them susceptible to multiple stator and/or rotor faults. ...
Induction motors (IMs) are widely used in industrial applications due to their advantages over other motor types. However, the efficiency and lifespan of IMs can be significantly impacted by operating conditions, especially Unbalanced Supply Voltages (USV), which are common in industrial plants. Detecting and accurately assessing the severity of USV in real-time is crucial to prevent major breakdowns and enhance reliability and safety in industrial facilities. This paper presented a reliable method for precise online detection of USV by monitoring a relevant indicator, denominated by negative voltage factor (NVF), which, in turn, is obtained using the voltage symmetrical components. On the other hand, impedance estimation proves to be fundamental to understand the behavior of motors and identify possible problems. IM impedance affects its performance, namely torque, power factor and efficiency. Furthermore, as the presence of faults or abnormalities is manifested by the modification of the IM impedance, its estimation is particularly useful in this context. This paper proposed two machine learning (ML) models, the first one estimated the IM stator phase impedance, and the second one detected USV conditions. Therefore, the first ML model was capable of estimating the IM phases impedances using just the phase currents with no need for extra sensors, as the currents were used to control the IM. The second ML model required both phase currents and voltages to estimate NVF. The proposed approach used a combination of a Regressor Decision Tree (DTR) model with the Short Time Least Squares Prony (STLSP) technique. The STLSP algorithm was used to create the datasets that will be used in the training and testing phase of the DTR model, being crucial in the creation of both features and targets. After the training phase, the STLSP technique was again used on completely new data to obtain the DTR model inputs, from which the ML models can estimate desired physical quantities (phases impedance or NVF).
... Some effects of the ITSC faults in the motors are, of course, malfunctioning in its operation, performance reduction, local magnetic saturation, and asymmetric behavior of the motor, to mention some [20,22]. With the purpose of better understanding the inter-turn short circuits, several investigations have developed models of these faults, such as the case of the finite element models (FEM) [18,23], parametric mathematical models based on electrical circuits [24,25], and combined FEM-parametric models [26,27]. The objective of such models is the development of methodologies for detecting such faults in simulation environments but assuming or omitting other conditions that could happen in real systems. ...
The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.
... Thus, thermography via a thermal camera was suggested for detecting the ITSC fault online. Later, Adouni, et al. [31] in 2020 extended this work by comparing the computed results with the temperature distribution under unbalanced supply voltage and Bento, et al. [32] in 2021 from the same research group further extended it by comparing the computed results with the calculated temperature of an LPTN model under the same ITSC fault conditions. Similar thermal models for ITSC fault conditions applied on the permanent magnet synchronous machines were proposed by Chen, et al. [33] in 2019 and Zhao, et al. [34] in 2021 employing the FEM approach as well. ...
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.
