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Cause and analysis of stator and rotor failures in tree-phase squirrel-cage induction motor
Abstract
The squirrel cage induction motor has limitations, which, if
exceeded, will result in premature failure of the stator or rotor. The
authors identify the various causes of stator and rotor failures. A
specific methodology is proposed to facilitate an accurate analysis of
these failures. Failures of the bearings and lubrication systems are
excluded
... TPIM often works in hostile environments such as corrosive and dusty places. Moreover, these motors are also subjected to mechanical, electrical, and thermal stresses during running conditions [9]. If the stresses become severe then various faults may initiate in the TPIM. ...
... According to [9], 35-40 % of IM failures are related to the stator winding insulation. Moreover, it is observed that a large portion of stator winding faults is initiated by insulation failures in several turns of a stator coil within one phase. ...
... According to the failure survey [9], it is stated that about 10% of total failure cases are related to rotor failures. Broken rotor bars do not initially cause an IM to fail but there can be serious secondary effects of broken rotor bars. ...
Electric motors have revolutionized the way of human living and resulted in the modern lifestyle. These motors often operate in corrosive and dusty places and are exposed to a variety of undesirable conditions and situations that result in the failure of the motor. The faults occurring in Induction Motors (IM) need to be detected at a proper time for avoiding losses and further consequences. A well-designed fault detection scheme not only reduces motor failure but also increases productivity and even sometimes avoids accidents. This paper presents a review of fault detection and classification techniques in three-phase induction motors (TPIM). The main theme of this paper is to revisit the conventional methods for fault detection in TPIM and compare them with recently published methods based on parameters to be sensed, and the type of fault that can be detected, with their advantages and drawbacks. Around a hundred papers are critically reviewed and studied from old and new regimes. Attention is also given to fault detection methods based on artificial intelligence (AI) and machine learning (ML). This paper concludes with brief remarks which will be very useful for new researchers who are willing to research in the domain of fault detection and classification.
... However, due to hazardous working conditions like contaminated working environment, fluctuating and stressed loading, lubrication deficiency, under or over voltage, and round the clock schedule operation, induction motors are prone to different types of faults or failures [5] [6]. Therefore, due to huge applications, and detrimental operating conditions of induction motor, its safe and reliable operation is the challenging task and extensive field of research to the electric motor researchers. ...
... Stator winding is the key part of induction motor that is responsible generates magnetic flux. Any deficiency in stator winding causes the non-uniform air gap flux that results into rising of winding temperature, unbalanced air gap flux, pulsating torque, voltage unbalance, end winding vibrations, and spectral variation in stator current [6] [32]. The main reason behind stator winding failure is the degradation of insulation material [11]. ...
... The main reason behind stator winding failure is the degradation of insulation material [11]. The stator winding defects may be classified as [6] [11] i) Inter-turn short, ii) Coil-to-coil short, iii) Line-to-line short, iv) Line-to-ground short, and v) Open winding faults Generally, stator defects start from turns-to-turn short of same coil, which further expanded to inter coils, then phase to phase and phase to ground short [33]. The different stator winding defects are shown in Figure 2. Various factors that are responsible to insulation failure are excessive thermal stress (sudden increase in winding temperature and loss of coolant), mechanical stresses (loose bracing for end winding, stator and rotor winding rubbing), electrical stresses (electrical expulsion, short circuits, high resistance connections) and chemical stresses (foreign particles, oil contamination, moisture, and dirt) [6]. ...
Objective: The classical support vector machine is not appropriate to classify large scale datasets. In this context, this paper presents a novel method utilizing large-scale active support vector machine (LASVM) to identify stator related defects of three-phase induction motor. Methodology: In this work, an experimental study has been performed to record the IM parameters as stator currents, and phase voltages at different health conditions of stator winding such as healthy stator (HS), voltage unbalance fault (VUF), open winding fault (OWF), and stator inter-turn short circuit fault (SISCF). Voltage unbalance index (VUI) parameter has been calculated using standard formula and utilized along with stator currents as features to train and validate the results using proposed method LASVM technique to identify stator defects. A comprehensive performance has been evaluated against other state-of-the-art sequential learning algorithms including online sequential extreme learning machine (OS-ELM), Least Square Support Vector Machine (LS-SVM) and Budgeted Stochastic Gradient Descent Support Vector Machine (BSGD) using popular time series regression and classification benchmarks. Results: Various performance parameters such as accuracy (ACC), sensitivity (SE), specificity (SP), and positive prediction value (PPV) have been determined to evaluate the effectiveness of proposed method. The simulated results have depicted that this method achieved classification ACC, 98.11%, 98.45%, and 96.35% for datasets HS-VUF, HS-OWF and HS-SISCF for radial basis function (RBF) kernel. The performance of this method was also found better in comparison to BPNN, online sequential extreme learning machine (OS-ELM), support vector machine (SVM), Budgeted Stochastic Gradient Descent Support Vector Machine (BSGD-SVM), and least square support vector machine (LS-SVM) methods.
... With fault tolerance becoming crucial, this design evolution holds promise for enhancing industrial system durability and performance [6]. Degradation of asynchronous motor bars is influenced by factors like direct starting and unexpected mechanical variations, causing thermal and mechanical stresses beyond design limits [7,8]. Softer starting methods and rigorous quality control are recommended [7]. ...
... Degradation of asynchronous motor bars is influenced by factors like direct starting and unexpected mechanical variations, causing thermal and mechanical stresses beyond design limits [7,8]. Softer starting methods and rigorous quality control are recommended [7]. Diagnostic signals, including stator current, Park vector, and mechanical vibration, offer crucial information for maintenance [9][10]. ...
The objective of this study is to evaluate the dependability of double-star induction machines by employing meticulous monitoring and intelligent handling of potential electrical malfunctions that may arise during their functioning. Specifically, the research delves into the examination of three distinct fault types: rotor bar breakage, stator phase opening, and inter-turn short circuit. Utilizing a mathematical model developed in the natural reference frame (abc), our aim is to comprehensively delineate both normal and faulty operational scenarios. To categorize and gauge the gravity of identified faults, we employ a methodology rooted in spectral analysis. In order to ensure continuous machine operation, especially in instances of stator phase opening, our proposed mitigation approach entails intentionally opening a secondary phase positioned at a 90-degree angle to the faulty phase. This strategic alteration transforms the machine into a dual two-phase configuration. Through rigorous simulation analyses, our findings underscore the efficacy and pragmatic viability of the devised fault detection technique, offering valuable insights into the domain of electrical machine reliability and fault management.
... The combination of different electric stresses generated through the transient voltages is particularly undesirable [137]. The stator is stressed by thermal, electrical, mechanical, and environmental factors, causing faults [139][140][141]. Table 4 presents the types of fault diagnostics techniques for induction motor (IM) stator faults. ...
... Table 4 presents the types of fault diagnostics techniques for induction motor (IM) stator faults. The stator is stressed by thermal, electrical, mechanical, and environmental factors, causing faults [139][140][141]. Table 4 presents the types of fault diagnostics techniques for induction motor (IM) stator faults. ...
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.
... If not detected, the propagation of the above phenomena can lead to excessive local overheating with the risk of dangerous arcing. Comprehensive descriptions and an analysis of stator faults mechanism propagation, introduced by a mixed effect of the above factors, are established in [8], and more recently in [9,10]. ...
Multiphase permanent-magnet motors are very attractive solutions for a large variety of applications, and specifically for electric vehicle applications. However, with a higher number of stator phases, multiphase permanent-magnet motors are more subjected to stator failures. Thus, diagnosing the stator status is necessary to guarantee the required efficiency of the motor. This paper deals with two techniques suitable for detecting and localizing open-phase faults in closed-loop controlled six-phase AC permanent-magnet motors. More specifically, this paper is aimed at assessing the diagnosis of open-phase faults based on current and voltage signature analysis. It is shown that the presence of specific harmonics can significantly affect the diagnosis process. Here, two diagnostic space vectors elaborated in the fifth α-β plane, based on the current and voltage signals, are proposed to cope with this limitation. The main contributions of the proposed approach are its implementation simplicity, and the effective immunity of the current-based analysis and voltage-based analysis against harmonic disturbances. The effectiveness of the proposed diagnostic space vector has been analyzed by numerical simulations, then experimentally validated.
... In terms of the various types of faults that can occur in IMs, the inter-turns short-circuit (ITSC) is the second most frequent fault, representing 37% of their total [5]. The main causes of stator insulation deterioration are linked to mechanical, environmental, electrical and thermal stresses [6]. When motors are driven by inverters, premature deterioration of the winding insulation tends to be more accelerated due to repetitive voltage peaks, of high magnitude and variation (dv/dt), which are imposed through the fast switching of power semiconductors [7]. ...
With the evolution of device condition monitoring methods, it is possible to make accurate evidence-based decisions and improve predictive maintenance and asset management strategies, thus increasing equipment availability and industrial process profitability. In this way, this paper aims to develop a methodology for the non-invasive detection of incipient short circuit between stator turns of field oriented control (FOC) driven induction motor, as well as enabling the monitoring of the evolution of fault severity. Among the contributions of this work, there is the proposition of a method based on the concept of discrete wavelet transform energy, and a method for selecting their respective decomposition levels that will be used in the composition of the fault detection indexes. The results obtained show that the developed technique is also effective for feedback driven motors (FOC) and are robust in relation to noise and control reaction mechanisms.
... There is a wide range of motor faults, including electrical faults, such as stator faults [3] and broken rotor bars [4], as well as mechanical faults, such as bearing faults [5] and air gap eccentricity [6]. According to an IEEE report [7], about 10% of motor faults are rotor-related. ...
Motor fault detection plays a vital role in industrial maintenance. Timely detection of faults in their early stages can prevent catastrophic consequences and reduce maintenance costs. Traditional methods face challenges in motor broken rotor bar (BRB) detection: model-driven methods are difficult to apply accurately in complex and changing environments, while data-driven methods usually require sophisticated feature extraction and classification processes. In this paper, we propose a novel non-invasive fault detection method. The method preprocesses motor currents by Hilbert-Huang Transform (HHT) and Park’s Vector Modulus (PVM) and then uses a merged convolutional neural network (CNN) for classification. This experiment investigates the detection of broken rotor bars of motors with different loads (25%, 50%, 75%, and 100% of rated load) and different fault levels (Normal, 1BRB, 2BRB, 3BRB, and 4BRB). The results show that the model’s classification accuracy exceeds 95% under various operating conditions and can maintain high accuracy under low load conditions, thus addressing the limitations faced by existing methods. In addition, it is computationally efficient and can guarantee high real-time performance. This method combines advanced signal processing techniques and deep learning algorithms to provide a practical solution for motor broken rotor bar detection.
... Due to the low impedance and high coupled flux linkage voltage, the ITSF windings create a high-fault current. This causes the stator to overheat and carry too much current [10], [11]. ...
Fault detection and diagnosis (FDD) is very important for making sure that electric cars (EVs) are safe and reliable. The electric motor drive and battery system, which store energy, are important parts of the EV's power train that can go wrong in a number of ways. If you don't find and fix these problems right away, they could cause EVs to stop working and even very bad crashes. Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have gotten a lot of notice for their use in electric vehicles. Because of this, finding faults in PMSMs, their drives, and lithium-ion battery packs has become an important area of study. An accurate, quick, sensitive, and cost-effective FDD method is what it takes to be successful. Modelbased and signal-based methods are two types of traditional FDD techniques. However, data-driven techniques, such as methods based on machine learning, have recently become popular because they seem to be good at finding faults. The goal of this paper is to give a full picture of all the possible problems that can happen in EV motor drives and battery systems. It will also look at the newest, most advanced study in finding EV faults. As a useful guide for future work in this area, the knowledge given here can be used
... Failures in electrical motors are common and difficult to prevent because motors are generally operated in industrial sites with different types of stress causing failures in various motor parts [4]. This has led to research on methods for early detection of failure in motors, to prevent motor inefficiencies and motor shutdown. ...
This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Na\"ive Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.
... These motors are essential for electromechanical energy conversion across various sectors, known for their durability and minimal maintenance needs. Despite their reliability, IMs can suffer premature failures due to excessive thermal, mechanical, and electrical stresses, with electrical issues in stators accounting for 30-40% of failures, rotor problems for 5-10%, and mechanical faults, including eccentricity and bearing issues, for 40-50%, as indicated by several diagnostic studies [2], [3]. This review will specifically examine SITFs, investigating their impact at different severities (0.3%, 0.7%, 1.05%, and 2.1%) under various load conditions. ...
... The induction machine is renowned for its robustness among electrical machines, facing various constraints of different types, including thermal, electrical, mechanical, and environmental factors. It can affect the life of the machine by causing the appearance of breakdowns in the stator and the rotor [2], [3]. Hence, it holds paramount significance to proactively identify potential defects in these systems. ...
The diagnosis and monitoring of industrial machinery faults is crucial to the Industrial Revolution but is frequently difficult and labor-intensive. Due to its quick computation, accurate prediction and robustness in performance, the use of artificial intelligence techniques has become a crucial component of condition monitoring of rotating machinery. This paper aims to use the artificial intelligence (neural network technique) to be able to detect and identify air gap eccentricity (static and mixed) and its influence on the stator current signature, the aim is to demonstrate the reliability and accuracy of these techniques to help us make the decision, The achieved simulation results allow distinguishing the types of eccentricity based on the Motor Current Signature Analysis technique data.
... The induction machine is renowned for its robustness among electrical machines, facing various constraints of different types, including thermal, electrical, mechanical, and environmental factors. It can affect the life of the machine by causing the appearance of breakdowns in the stator and the rotor [2], [3]. Hence, it holds paramount significance to proactively identify potential defects in these systems. ...
... In comparison, 40% to 50% are linked to mechanical issues, and the remaining are associated with rotor failures and others [3,4]. Most statorrelated failures are induced by thermal, electrical, mechanical, and environmental stresses, according to Siddique [5,6], which leads to line-to-ground and line-to-line short circuits. Nonetheless, this kind of fault starts with inter-turn short circuits (ITSCs), followed by large currents through this low impedance path, producing high local temperatures and affecting the surrounding insulation [7]. ...
Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.
... If the engine stays on standby for long, it may trigger shocks when activated. [22][23][24][25] The system will experience losses. Failure of a component that occurs only in a standby state is described as ''standby failure 26 Ruiz et al. 27 investigated the problem of standby components inventory, considering the effects of components suffering from on-shelf deterioration. ...
A novel maintenance policy for a two‐component warm standby system with multiple standby states is presented in this paper. Two standby states, that is, cold and warm standby, for components in the system are considered. Components can realize the transition from the cold standby to the warm standby state by periodic switching, intended to shorten recovery time and save system operating costs. Preventive maintenance (PM) and preventive switching (PS) of components are considered. In the PS strategy, the standby component can switch before the operating component fails. In addition, additional standby failure modes based on idle time are studied. Derive the long‐term average cost of the system through the semi‐regenerative process. A numerical example eventually verifies the feasibility of this paper's proposed maintenance and switching strategy.
... Eventually, this leads to failure of the entire operating system if the failure condition is not identified or if it is neglected. Several types of faults related to winding, stator, rotor, and bearing can be observed in an induction motor [2,3]. There are mainly four types of fault diagnosis methods such as signal-based, model-based, knowledge-based, and active/hybrid methods. ...
Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor's fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.
... As shown in Figure 2, the shorted turns create an additional circuit loop connected to flux linkages created by other motor windings and the rotor magnet. A high-fault current is created in the ITSF windings because of the low impedance and high-coupled flux linkage voltage leading to stator overcurrent and overheating [10,11]. At the early stages of the ITSF, with failure in only a few percentages of turns, the motor can continue to operate with degraded performance. ...
Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.
... As a consequence, the service life of the windings decreases by 50% for every 10 • C increase above the allowable temperature limit [4,5]. In addition, the temperature increase results in the reduction of motor efficiency and power. ...
This study provides an overview of new trends in the development of cooling systems for electric motors. It includes a summary of academic research and patents for cooling systems implemented by leading motor manufacturers at TRL9. New trends in the cooling management of air and liquid cooling systems are discussed and analyzed with a focus on temperature distribution and its influence on the power-to-dimension ratio of electric motors. The prevailing cooling method for synchronous and asynchronous motors is air cooling using external fins, air circulation ducts, air gaps, and fan impellers to enhance efficiency and reliability. Internal cooling with rotor and stator ducts, along with optimized air duct geometry, shows potential to increase the power-to-dimension ratio and reduce motor size. Liquid cooling systems offer a power-to-dimension ratio of up to 25 kW/kg, achieved through redesigned cooling ducts, stator heat exchangers, and cooling tubes. However, liquid cooling systems are complex, requiring maintenance and high ingress protection ratings. They are advantageous for providing high power-to-dimension ratios in vehicles and aircraft. Discussions on using different refrigerants to improve efficient motor cooling are underway, with ozone-friendly natural refrigerants like CO2 considered to be promising alternatives to low-pressure refrigerants with high global warming potential.
... The rotor of a squirrel cage induction motor is susceptible to various stresses that can lead to failures [13]. Some of these main defects are shown in Figure 1. ...
... However, they all have similar fault types and distribution [8]. Amongst all the faults of these wind generators, the winding fault, as the second most frequent fault, has attracted significant interest in the past 30 years [8][9][10][11][12][13][14][15]. It has been reported by the authors in [8] and the Electrical Apparatus Service Association (EASA) that there are five major winding faults: (1) inter-turn (turn-to-turn) short circuit (ITSC), (2) coil-to-coil short circuit, (3) open circuit of one phase, (4) phase-to-phase short circuit, and (5) coil-to-ground short circuit. ...
This paper proposes a general analytical model for large-power surface-mounted permanent magnet (SPM) wind generators under inter-turn short-circuit (ITSC) faults. In the model, branch currents rather than phase currents are used as state variables to describe the electromagnetic behavior of the faulty machine. In addition, it is found that the multiphase Clarke transformation can be used to simplify the proposed fault model with the inductances calculated analytically or numerically using finite element analysis. With the latter, both linear and nonlinear inductances can be obtained, and the non-linear inductances are used for the fault modelling of large power rating machines due to larger electrical loading and heavier magnetic saturation. With the developed fault model, studies of scaling effects (different power ratings such as 3 kW, 500 kW and 3 MW) and the influence of fault location on the electromagnetic performance of SPM generators with series-parallel coil connections have been carried out. The simulation results show that large-power SPM wind generators are vulnerable to ITSC faults when a relatively small number of turns are short-circuited and a single-turn short-circuit fault at the top of the slot is found to be the worst case.
... It is estimated that the stator faults constitute 21% of all the faults [8]. Stator faults usually start as interturn short circuit faults (ISCF) [9] and quickly develop further into complete phase-to-phase or phase-to-ground faults, which implies the total malfunctioning of the machine. Depending on the machine and the fault's structure, the time between ISCF occurrence and the total loss of insulation is in the order of seconds [10]. ...
p>Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime of an electrical machine. In an induction machine (IM) driven by an inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced by a fault due to the fault-controller interaction. Based on this observation, this study developed neural network models using inverter switching statistics to detect the ISCF of an IM. The method was non-invasive, and it did not require any additional sensors. In the fault detection task, an area under receiver operating characteristics curve value of 0.9950 (95% Confidence IntervaI: 0.9949 - 0.9951) was obtained. At the rated operating conditions, the neural network model detected and located an ISCF of 2-turns (out of 104 turns per phase) under 0.1 seconds, a speedup of more than two times compared to the thresholding-based method. Moreover, we published the switching vector data collected at various load torque and shaft speed values for healthy and faulty states of the IM, becoming the first publicly available ISCF detection dataset. Together with the dataset, we provided performance baselines for three main neural network architectures, namely, multi-layer perceptron, convolutional neural network, and recurrent neural network.</p
... ISCFs occur when the insulation between two turns degrades owing to a variety of causes, resulting in electrical contact between the turns. This results in an increased current in the exposed turns, which in turn causes a significant increase in temperature (hot spot), hence, more erosion of turns and finally complete stator windings failure [8]. The time between the onset of ISCF and the complete machine failure is very critical as it is the only available time during which a complete machine failure could be avoided [9], [10], it ranges from a few seconds to several days [11], this encourages more research towards early ISCF detection [12]- [15]. ...
Induction Motor (IM) restoration costs and downtime can be decreased by early Inter-turn short circuit fault (ISCF) detection. Due to the controller’s innate desire to generate an adjusted set of currents actually below fault conditions, fault detection of electric motors driven by an inverter with a model predictive control (MPC) algorithm becomes more difficult in inverter-driven applications. We suggest a novel actuation method in this contribution using the switching sequences produced by the Finite Control Set Model Predictive Controller (FCS-MPC) for ISCF of IM. based on diagnostics from neural networks (NN). Hence, no extra sensors or equipment are required for fault detection. This paper proposes a novel procedure for ISCF fault location of IM based on Neural Networks with Learnable Leaky ReLU (LeLeLU) function.
Industries are heavily dependent on rotating components, which are gradually deteriorate over time, resulting in system failure and financial losses. Numerous investigations have been conducted utilizing various methodologies to identify incipient faults in rotating components. But more focus is given to single component fault diagnosis. It is highly unlikely that one component fault will occur at a time in real-world situations. Multiple components and sub-component faults take place simultaneously in a system. The signature of multi-fault conditions is chaotic, and diagnosis is difficult with signal processing techniques as fault information is modulated. Some studies have been done to identify the multi-faults of rotating machines using different approaches. However, a dedicated literature review on multi-fault diagnosis has not been presented properly. Therefore, this paper discusses the advancement in multi-fault diagnosis by implementing a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article reports the article selection strategy, inclusion and exclusion criteria, etc. in a transparent manner. It also discusses the trends, techniques and approaches for multi-fault diagnosis. It is found that, bearing and gear multi-fault are simulated a lot with constant speed conditions. Vibration based analysis with data driven approaches are used enormously for multi-fault diagnosis. After analysis, this article reports the research gaps, and challenges and gives recommendations for the wide audience to work in the future. This systematic literature review will be helpful for researchers to find solutions that are more practical and can be implemented in industrial scenarios.
This work describes a novel and effective application of the adaptive wavelet transformfor the detection of bearing faults on induction motor stator current. This transform is based on athree-step nonlinear lifting scheme: a fixed prediction followed by a space-varying update and a noadditive prediction. This transformation technique is used in a diversity of applications in digitalsignal processing and the transmission or storage of sampled data (notably the compression of thesound, or physical measurements of accuracy). Many faults in induction motor have beenidentified as bearing defects, rotor defects and external defects. Experimental results confirm theutility and the effectiveness of the proposed method for outer raceway fault diagnosis under noload and full load conditions.
This article presents an analytical model of the interturn short-circuit fault in interior permanent magnet synchronous motors (IPMSMs) with multistrand windings. The proposed model covers possible failure modes due to insulation degradation. With a coil as the basic unit, this model has considered the spatial layout and mutual inductance of motor windings. Compared to other mathematical models, this model has made substantial progress in authenticity and reliability. The model is controlled through closed speed and current loops, which is consistent with most application scenarios. This model can be used to study the postfault behavior of IPMSMs under transient and steady states. An experimental setup is built, and validation experiments under various fault modes and operating conditions are conducted. The fault currents obtained using the proposed model are in good agreement with the experimental results, confirming the value and potential of the proposed model in understanding such faults and developing automatic fault diagnosis methods.
The most popular low voltage starters for 50 Hz industrial motors are either star or delta starters. Their purpose is to minimize the starting current that is supplied to the motor at the beginning of operation. Three contactors, a timer, and a thermal overload are typically used in the manufacturing of Star/Delta starters, which are used to run three-phase motors at 440 volts and 50 Hz AC mains supply. But in our project, we've used the same to run a\ 3-phase motor at 440-volt AC mains supply 50 Hz with a series of 12-volt DC relays, an Arduino-provided electronically adjustable timer, and a series of tiny circuit breakers. While maintaining its application for a 3-phase motor starting with a single phasing prevention, the interlocking arrangement of the relay coils and the electronic Arduino is all wired in low voltage DC of 12 Volt fed from an inbuilt DC power supply for safe handling of the starter during the study. The timer consists of an Arduino, whose output is sent to a relay to switch the mains supply from a three-phase star to a delta. Since the 3-phase motors could burn if one of the phases disappears while they are operating, the project additionally includes single-phase protection. In the case of a phase failure, the lamps' output will be switched off. In addition, the project can be improved by utilizing thyristors in a firing angle control principle to soft start the induction motor, which would eliminate all of the star delta starter's shortcomings.
This paper proposes an improved thermal modeling approach for fractional-slot concentrated winding (FSCW) permanent magnet (PM) machines, based on the thermal network method (TNM). In the traditional TNM models, the core loss is considered uniformly distributes in the stator core, which actually leads to an unavoidable error in FSCW PM machine thermal prediction. In order to achieve a high accuracy, the discrete loss distribution model is built for machine stator, where the stator core is divided into amounts of blocks. Based on the Bertotti model, the accurate core loss distributions are calculated under different load rates. Then, the calculated core losses are allocated to corresponding blocks, and the temperature rise is predicted by TNM. The predicted results by the proposed model reveals that there exists the local hot spot in the stator tip caused by the local loss concentration in FSCW PM machines, which is not ever reported. In order to validate the predicted results, the experimental platform with a 12-slot 10-pole FSCW PM machine is built and tested. The experimental results reveal that the proposed model features a higher temperature prediction accuracy, while the local hot spot really exists in stator tip. It is believed that the proposed model can give a more reliable support in FSCW PM machine design considering magnetic-thermal coupling.
Voltage stress across motor winding is critical for insulation health when its series resonance frequencies, which have the least impedances, known as antiresonances, coincide with overvoltage (OV) resonance frequencies across it, named as antiresonance phenomenon (ARP). First, this article discloses that the OV resonance frequencies across motor winding (load) can be represented by the combination impedance of cable plus load. Second, it investigates the interactions between the cable and load impedances in different layouts and examines their impact on the ARP. Then, it discloses the sensitivity of ARP versus cable and load impedance parameters. Lastly, the ARP's correlation with these parameters establishes a safe operation area as a motor drive system design guideline. Contrary to the general belief that systems using short cable or integrated systems have less insulation damage, this study shows that the OV stress in these systems can be critical. This article offers a simplified methodology to optimize the reliability of the drive system and mitigate the ARP. By using this approach, the article suggests that the time-consuming iterative design of
dV/dt
filters or overdesign of insulation can be eliminated. The practical test and modeled system are conducted to validate the approach.
Os motores de indução estão extremamente difundidos no setor industrial, estando presentes nos mais diversos equipamentos, exercendo importantes funções para os processos produtivos. E assim como qualquer outra máquina, essa está sujeita a falhas, o que pode ocasionar na parada da linha de produção, gerando prejuízos para a empresa, além de ser um risco em potencial para aqueles que operam o equipamento. Visando evitar tal situação, a manutenção por condição, também conhecida como manutenção preditiva, vem ganhando cada vez mais espaço no setor de manutenção industrial, já que essa tem como objetivo identificar a falha antes que ela ocorra. Aliada a essa metodologia, estão presentes poderosos algoritmos de aprendizado de máquina, que são capazes de identificar e classificar com alta precisão o estado do equipamento, e a depender do caso, a gravidade do defeito presente. Diante disso, esse trabalho tem como objetivo, fazer uma revisão bibliográfica acerca do tema, trazendo dessa forma, conceitos importantes, além de expor os principais defeitos em motores elétricos e os principais algoritmos que estão sendo aplicados na resolução do problema.
This abstract discusses research conducted at the University of South Florida's Electrical Engineering department, focusing on the electrical aspects of electric drives. It highlights the significance of temperature variations on the performance of electric machinery and identifies three main control techniques used in electric drives: Scalar control, Direct Torque control, and Field Oriented control.
The thesis investigates the temperature rise in various components (rotor bars, stator winding, stator core, and stator frame) of a three-phase field-oriented controlled induction machine during operation. Notably, there is limited prior research on the thermal response of a field-oriented controlled induction motor. To address this gap, the researchers developed a lumped parameters thermal model using MATLAB Simulink. The findings show that rotor bars experience the highest temperature increase, reaching up to 84 degrees Celsius, consistent with existing literature.
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.
Among the many great features of induction motors are their simplicity, reliability, efficiency and high robustness. For these, they are extensively employed in various applications and industrial fields. Nevertheless, motors, as it is the case for other appliances and equipment, are exposed to various types of faults. Consequently, these faults must be monitored, classified and diagnosed in order to prevent possible disastrous failures of the machine itself and the whole system as well. This paper explores the various faults that motors are vulnerable to by performing the appropriate tests. The results were obtained by collecting the different detected response signals.
Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime of an electrical machine. In an induction machine (IM) driven by an inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced by a fault due to the fault-controller interaction. Based on this observation, this study developed neural network models using inverter switching statistics to detect the ISCF of an IM. The method was non-invasive, and it did not require any additional sensors. In the fault detection task, an area under receiver operating characteristics curve value of 0.9950 (95% Confidence IntervaI: 0.9949 - 0.9951) was obtained. At the rated operating conditions, the neural network model detected and located an ISCF of 2-turns (out of 104 turns per phase) under 0.1 seconds, a speedup of more than two times compared to the thresholding-based method. Moreover, we published the switching vector data collected at various load torque and shaft speed values for healthy and faulty states of the IM, becoming the first publicly available ISCF detection dataset. Together with the dataset, we provided performance baselines for three main neural network architectures, namely, multi-layer perceptron, convolutional neural network, and recurrent neural network.
This paper describes the practical application of a portion of a threory of .multisegmental rotor bar analysis' as published by Diamant (A new appraoch to multisegmented rotor slot design for induction motors, IEE Trans, 1972). A special rotor bar-slot configuration was developed, and patented. to improve accelerating torque of several large high speed motors. Manufacturing considerations associated with this shape are described in detail. Because of several limitations inherent in existing test facilities, special test equipment and methods were then devised to confirm the degree of improvement; these have since been successfully applied to other machines as well. (from Author's abstract)
The paper presents an analysis of stray losses in polyphase cage-induction motors, under linear and saturation magnetisation conditions, with particular attention being given to the effects of circumferential currents flowing in the rotor as a consequence of imperfect bar-iron insulation. Such effects are included in a modified equivalent circuit of the motor, and are expressed in terms of factors readily determined from a set of normalised curves, not previously available. For example, the consequent reduction in the effective skew of the rotor bars can be determined, and thus a more correct evaluation obtained for those factors and losses dependent upon the resultant skew. Also, a simple graphical method is given for determining the effective increase of the air gap due to main-flux saturation, which assists in demonstrating that favourable agreement between the predicted and experimental torque/slip characteristics can be obtained with some reduction of the empiricism employed hitherto.
The deep-bar rotor may be defined as a single-cage rotor in which skin effect plays a prominent part. With rotor slots nearly one inch deep, skin effect reduces starting current and increases starting torque so that in many ratings this type of motor has superseded the older shallow cage motor. This paper will endeavor to supply the theory of the tapered bar and of bars with a tapered section.It also will endeavor to improve the theory of the round bar. A graphical method for computing the impedance of any shape of bar will be given.The formulas developed indicate that further improvements should result if the bar is somewhat enlarged near the slot opening.It will be hown that: 1) The tapered bar is superior to the rectangular bar, provided the narrow portion is near the top. 2) Further improvements should be sought among bars of compound shape. 3)The slot constant,for a round slot is close to 0.72when running.
First Page of the Article
The ″Linear Equi-Permeance Rotor Slot″ concept and Grapho-Visual design-analysis techniques, for 2-segmented slots and the ″Linear Equi-Permeance Slot″ Model Theory are presented. The ″Linear Equi-Permeance Equation″ for multi-segmented slots are examined perUnit notation and the nature of the T-bar slot and vector-interpretation and ″Equi-Area″ transformations are examined. Performance analyses of 3 and 4-segmented slots and test-proven optimization of the speed-torque-current relationship and the overall motor-design are discussed. The Model Theory is extended.
The first step of the presented method development has been, in Section 2 of Part I, the "ddcoupling" of slot segments in slot-constant calculations, using segmental " Weighing Factors". The direct practical application of the Decoupling Method is restricted to rectangular-top two-segmented slots, having rectangular or tapered bottoit segments. Iteration of this method, indirectly, allows the handling of further rectangular, "upper" and "peak" segments too.
A discussion of the losses caused by skewing of a rotor with uninsulated bars is presented, giving full consideration to the distributed nature of the rotor parameters. At first, tooth pulsation and surface losses are reviewed briefly. Then an approximate method of calculation of losses due to iron cross currents in the rotor is presented and shown to yield experimentally acceptable results. The equations for the latter losses are relatively simple and the effect of parameter variations can be studied. The losses in the bars are shown to be inversely proportional to the square of the differential leakage coefficient of the rotor; the surface losses in the iron are a function of the resistance between adjacent bars of the squirrel cage and are inversely proportional to the first power of the differential leakage coefficient.
Every motor user is faced with the fact that someday his motor will fail. Due to the destructive nature of most failures, it is not easy to determine the cause. The purpose of this paper is to introduce a procedure for analyzing these failures and determining the cause so that further steps can be taken to eliminate them.
The practical application of a portion of a theory of ``multisegmental rotor bar analysis'' as recently published by Diamant [1] is described. A special rotor bar-slot configuration was developed and patented to improve the accelerating torque of several large high-speed motors. Manufacturing considerations associated with this shape are described in detail. Because of several limitations inherent in existing test facilities, special test equipment and methods were then devised to confirm the degree of improvement; these have since been successfully applied to other machines as well.
The results are presented of a computer study to determine various operating quantities of squirrel-cage induction motors as a function of unbalanced voltage and mechanical loading utilizing a new analytical technique. A similar study with the extreme unbalance of single phasing was reported elsewhere. The operating characteristics of various motor protective devices are then superimposed on the motor operating curves to evaluate the effectiveness of the specific relay. The results of this study will aid in selecting and setting relays to provide protection against voltage unbalance and may possibly provide an engineering rationale for the development of future protective devices.
The use of aluminum in fabricated cage windings of large induction motors has been explored, and it was found that aluminum and its alloys present a viable alternative to copper and its alloys. This paper enumerates the advantages and limitations of aluminum as a cage material, outlines the considerations necessary in the utilization of the material for this application, and summarizes the results of a testing program which demonstrated the adequacy of aluminum alloy cages in full scale machines.
Failure mechanism of rotating machines in indus-trial service Types of insulation breakdown
- R M Sexton
- Tum Sulvey
- Insulation
- A C Large
- W Motors
- M Schneider
- Olphant
- Jr
R. M. Sexton, A Sulvey of Tum Insulation on Large A. C. Motors, W. Schneider, " Failure mechanism of rotating machines in indus-trial service, " Voltage, Jan. 1964. M. Olphant, Jr. " Types of insulation breakdown, " 3M Electrical Shorts.
Vibration, causes and effects on large electric motors
- J Nevelsteen
J. Nevelsteen, " Vibration, causes and effects on large electric motors, " Paper #PCI 78-26.
IS a 25% margin for critical speed satisfactory?
- H M Carbone
- J D Edick
- E C Labrush
H. M. Carbone, J. D. Edick, and E. C. LaBrush, " IS a 25% margin for critical speed satisfactory?, " in Conf. Rec. 78CH1322-7 IA, Paper PCI-78-6.
The influence of iron on squirrel cage bar heating Elekrrotechnik Fabricated aluminum cage construction in large induction motors IEEE Conf Tran-sient behavior of induction motor rotor cages Protection of induction motors against unbalanced voltage operation
- Apparatus
- I H Mar
- J L Olbrisoh
- Craggs4-La
- Pci-R Paper
- J Blanchardie
- M Chatelain
- M Jufer
- P G Pasdeloup
- J R Cummings
- R H Dunki-Jacobs
- Kerr
Apparatus, Mar. 1973. I. H. Olbrisoh, " The influence of iron on squirrel cage bar heating, " Elekrrotechnik, Dec. 1955. J. L. Craggs, " Fabricated aluminum cage construction in large induction motors, " IEEE Conf. Rec. 73CH0989-4-LA, Paper PCI-R. Blanchardie, J. Chatelain, M. Jufer, and M. Pasdeloup, " Tran-sient behavior of induction motor rotor cages, " French Soc. Elect., Apr. 1966. P. G. Cummings, J. R. Dunki-Jacobs, and R. H. Kerr, " Protection of induction motors against unbalanced voltage operation, " Gen-eral Elec. Co., PCIC-83-3.
What high torque? Consider the double cage motor
- R L Nailen
R. L. Nailen, " What high torque? Consider the double cage motor, " Power Enc. ADr. 1971. 75-8.
Recent developments in the technology of rotor cages for induction motors
- J Bichet
The cause of rotor pullover-And how to cure the problem
- R L Nailen
Sparking of ac motor rotors and its effect on division 2 application
- E F Merrill
- C R Olson
Types of insulation breakdown
- Olphant Jr
Stop rotor troubles before they start
- R J Nailen
The design of reliable squirrel cage rotors
- K K Schwartz
Faltering pulse can reveal an ailing motor
- B G Gaydon
- D J Hopgood
Transient behavior of induction motor rotor cages
- R Blanchardie
- J Chatelain
- M Jufer
- M Pasdeloup
Are electric motor aluminum bar rotors here to stay?
- R L Nailen
The influence of iron on squirrel cage bar heating
- I H Olbrisch
Design of large induction machinery using fabricated aluminum rotor cages
- G C Soukup