![Stator current Clarke vector pattern [10].](https://www.researchgate.net/profile/Toomas-Vaimann/publication/258547195/figure/fig6/AS:668629108539394@1536425010243/Stator-current-Clarke-vector-pattern-10_Q320.jpg)
![Magnetic field distribution in the cross section of a 3 kW induction motor at nominal load operating condition: left-healthy rotor cage, rightfaulty rotor cage with seven broken bars (shaded) [13]](https://www.researchgate.net/profile/Toomas-Vaimann/publication/258547195/figure/fig8/AS:668629108551687@1536425010297/Magnetic-field-distribution-in-the-cross-section-of-a-3-kW-induction-motor-at-nominal_Q320.jpg)
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Sensorless Detection of Induction Motor Rotor Faults Using the Clarke Vector Approach
Abstract and Figures
Due to their rugged build, simplicity and cost effective performance,
induction motors are used in a vast number of industries, where they
play a significant role in responsible operations, where faults and
downtimes are either not desirable or even unthinkable. As different
faults can affect the performance of the induction motors, among them
broken rotor bars, it is important to have a certain condition
monitoring or diagnostic system that is guarding the state of the motor.
This paper deals with induction motor broken rotor bars detection, using
Clarke vector approach.
Figures - uploaded by Toomas Vaimann
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Content may be subject to copyright.
... Many publications [4][5][6][7] investigated the condition monitoring (CM) and fault diagnostics (FD) of IM faults related to electrical, mechanical, and environmental scenarios Many publications [4][5][6][7] investigated the condition monitoring (CM) and fault diagnostics (FD) of IM faults related to electrical, mechanical, and environmental scenarios when operated with the grid supply. However, inverter-fed machines also undergo faults due to the impacts of the Pulse Width Modulation (PWM) technique, which has a highfrequency transition, and high steepness in the waveform or high voltage gradient (dv/dt) due to switching frequency (fsw) of power electronic wide band-gap drives (WBG). ...
... Many publications [4][5][6][7] investigated the condition monitoring (CM) and fault diagnostics (FD) of IM faults related to electrical, mechanical, and environmental scenarios Many publications [4][5][6][7] investigated the condition monitoring (CM) and fault diagnostics (FD) of IM faults related to electrical, mechanical, and environmental scenarios when operated with the grid supply. However, inverter-fed machines also undergo faults due to the impacts of the Pulse Width Modulation (PWM) technique, which has a highfrequency transition, and high steepness in the waveform or high voltage gradient (dv/dt) due to switching frequency (fsw) of power electronic wide band-gap drives (WBG). ...
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.
... At the moment, condition monitoring of electrical machines and drives has become one of the norms in the industry [5][6][7], which is now moving towards predictive maintenance [8] [9]. Usually, the maintenance of machines can be divided into four different phases including reactive, periodic, proactive and predictive maintenance, with the industry now moving towards proactive and predictive maintenance from periodic and reactive maintenance, as shown in Fig. 1. ...
The industrial revolution has opened up more paths with the integration of information technology with industrial applications. Similarly, most industrial processes can be streamlined by combining the Internet of Things (IoT) and artificial intelligence. Artificial intelligence has a significant role in this development, whether it is related to real-time condition monitoring of electrical machines or switching of the industry from scheduled maintenance to predictive maintenance. One of the main challenges for artificial intelligence is the quality and quantity of data used for training models, as it requires big data sets to train more accurate and efficient models. This paper presents a data acquisition system with real-time condition monitoring of electrical machines. A comparison between trained models from real signals and synthetic signals, generated through the equation, is also covered in this paper. This is to help identify whether utilizing synthetic signals for the training of fault diagnostics models can be a good alternative in the long run or not.
... IoT applications in the industry have further increased from data monitoring towards predictive maintenance with development in industrial equipment. After condition monitoring [4] [5] which has become quite common in the industry, predictive analytics for maintenance [6] using the collected data for machines is the next advancement. The maintenance of machines can be divided into reactive, periodic, proactive and predictive phases (see Fig. 1). ...
Internet of Things (IoT) has become the need of the hour with the recent advancement in technology. The emergence of new technologies has helped to communicate between different machines and it has become easier to interact with them. This has helped with the reduction of maintenance costs and the time needed to fix the machine. Furthermore, it is necessary to monitor the impact of surrounding factors on electrical machines and detect any faults as soon as possible. With the integration of artificial intelligence and IoT, a system can be built to help detect faults as soon as it happens and help contain the impacts of such faults. In this research, a low-cost, efficient method to monitor any electrical machine and detect faults in real-time is being explored, which can help reduce maintenance charges. In addition, it can also help reduce the impact of the generated fault. For the research purpose, the setup consists of an induction motor and broken bars faults are considered for training models and detecting faults in real-time.KeywordsInternet of ThingsIoTElectrical machinesCondition monitoringFault detection
... Industrial standard 4.0 has given way to the implementation of condition monitoring [3,4] at a mass scale in the industry, leading toward predictive maintenance [5,6] of electrical machines in the near future. Many companies are working on different predictive maintenance algorithms to reduce their scheduled maintenance costs. ...
Nowadays, most domestic and industrial fields are moving toward Industry 4.0 standards and integration with information technology. To decrease shutdown costs and minimize downtime, manufacturers switch their production to predictive maintenance. Algorithms based on machine learning can be used to make predictions and detect timely potential faults in modern energy systems. For this, trained models with the usage of data analysis, cloud, and edge computing are implemented. The main challenge is the amount and quality of the data used for model training. This chapter discusses a specific version of a condition monitoring system, including maintenance approaches and machine learning algorithms and their general application issues.
... As manufacturing has been advancing, IoT applications related to industrial development and monitoring have been increasing rapidly too. The inclusion of data collection from machines to monitor them [4], [5] and run predictive analytics for maintenance [6] is becoming a norm in the industrial field. Due to the advancement in this field, maintenance has been divided into several stages as described in Fig. 1. ...
... In [10], there is proposed a method for reducing common-mode voltage and bearing currents in quasi-resonant DC-link inverter. Authors in [11] provide sensorless detection of induction motor faults using the Clarke vector approach. ...
This paper describes possibilities for reduction of bearing currents in induction motors. Growing usage of motors running with variable speed drives, the problems of bearing currents and related damages have become a significant issue in industry. Bearing currents cause a higher portion of faults in induction machines. Therefore, the maintenance question is getting a great importance. The paper addresses a problem of bearing currents and lists different methods for their avoidance. This study gives a comparison of the methods, discussing advantages and disadvantages of each of them.
... Hence, already in the beginning of the 20st century, the existence and reasons behind classical bearing and shaft currents were known. Those reasons are mainly related to constructional asymmetries of the motors, such as static and dynamic eccentricity, constructional asymmetries, unbalanced supply, connections of laminations, rotor faults [8]- [10]. The latter has been described in [11] through simulations, where broken rotor bars are inducing eddy currents in the shaft inevitably leading to bearing currents and damages. ...
Bearing damages make the biggest proportion of faults affecting AC electrical motors. There are various subdivisions of the bearing faults but one of them is the induced bearing and shaft currents in the machines. These currents cannot be fully avoided, yet with the development of power electronic control of the machines, the number of machines affected by potentially more dangerous and damaging frequency converter induced bearing and shaft currents is growing rapidly. This paper is opening the background of the nature and types of bearing currents, lists the damages inflicted by these currents and addresses problem as well as stresses the necessity of taking those currents into account for ensuring more efficient and longer lifespan of industrial electrical machines.
When spindle surface is irregular, the rotation error cannot be obtained by measuring the spindle. Therefore, a new method on rotation error measurement and rotation error compensation based on Clarke transformation was developed by full application of geometry shape of the regular hexagonal section shaft. Experimental result shows that the measurement results have consistency highly when the motor speed is 0.4r/s,and when rotating spead is less than 1r/s, the fluctuation range of length and Angle of spindle error in polar coordinates is 0.01mm and 0.01rad at different motor speed basically.Meanwhile, a comparative experiment is designed to verify the accuracy.The experiment shows that measurement error is within 0.01mm basically and the measurement error is controlled within 0.01mm with a success rate of 84% and 0.015mm with a success rate of 96%. So the model can be applied to the straightness measurement process of the regular hexagonal section shaft directly.
The rotor parameters, the resistance and the leakage inductance, vary with the currents frequency of rotor bars due to skin effect, and for small frequencies can be negligible (under 10 15 Hz); but for higher frequencies this variation of the parameters cannot be negligible. In this paper, for higher frequencies some analytic expressions are proposed, that result from the skin effect theory, for the rotor resistance and for the leakage inductance. The simulations were done by SYMNAP-Symbolic Modified Nodal Analysis Program, a program that can determine, in a full-symbolic form the voltages and the currents for the equivalent scheme for one phase of the asynchronous motor. Symbolic expressions for the characteristics quantities of the general regime of the induction motor allow graphic representation for different parameters. These analytic expressions allow a more accurate analysis of asynchronous motor performance for different drive systems. To simulate the dynamic (transient) behaviour of the induction motor, Park-Blondel equations have been used; these equations have a nonlinear form due to both their structure and frequency variation of the rotor parameters. The integration of these equations is performed with the help of the integration routines for ordinary differential equations in Matlab programming environment. It was found that the nominal values of the quantities that characterize the induction motor operation, obtained by simulation, practically coincide with the values from the catalogue.
During the past few decades there has been a continually increasing interest and investigation into fault detection and diagnostics of induction motors. There are various
different diagnostic approaches and methods to detect faults in induction motors. Attention should be paid to the fact that as induction motors are often used in critical duty drives, it is essential not to disturb the working cycle of the motor in any way while performing the needed diagnostic procedures. The needed measurements have to be carried out with the motor remaining in its normal operating mode, as changing of the working cycle can mean safety risks and economic losses in some applications where induction motors are used. This paper gives an overview of the sensorless diagnostic possibilities of three-phase squirrel-cage induction motors with broken rotor bars via the analysis of stator current.
Broken rotor bars and end-ring are common faults in three-phase squirrel-cage induction motors. These faults reduce the developed toque and increase the speed fluctuations of the motor. Meanwhile, developed unsymmetrical magnetic generates noise and vibration in the motor. Local heat around the broken bars may gradually breakthe adjacent bars and the motor will be finally out of service. Finite element method (FEM) is the most accurate technique for diagnosis and analysis of induction motor, because it can include all actual characteristics of the healthy and faulty induction motors. However, current density is generally considered as input for performance computation process, while fault can inject a large harmonics to the stator current. These harmonics may not be ignored in the fault diagnosis of the motor. In addition, all FE applications consider the steady-state mode of operation. In this paper, a three-phase voltage-fed squirrel-cage induction motor with rotor broken bars is proposed and analyzed for the starting period of the motor. Both no-load and on-load cases are considered. Also, concentrated rotor broken bars under one-pole and the distributed rotor broken bars under different poles are studied and compared.
Due to their reliability, robustness and simple construction squirrel-cage induction motors are widely used in industry and are one of the most critical components in industrial processes. Any motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, and safety. Among several detection approaches proposed in the literature, those based on stator current monitoring are advantageous due to their non-invasive properties. One of these techniques is based on the spectrum analysis of machine line current. Another non-invasive technique is the Park's vector approach. In the literature this method is mainly used for detecting the stator winding faults of the induction machine In this paper the way how the Park's vector approach based method can be used for detecting the rotor faults of the squirrel cage induction machine is presented. After a short theoretical presentation results obtained by laboratory tests are given.
Diss. -- Lappeenrannan teknillinen yliopisto.
The paper presents on-line condition monitoring and diagnostic system of induction motor drives. It enables a detection of broken rotor bars at an early stage of the fault propagation. The method is based on the analysis of stator current frequency spectrum, which can be measured without disturbing normal motor operation. Therefore it is completely non-invasive and easy to implement in industrial environments. The presented monitoring system is applied on six motors (1200 kW and 2000 kW) used for water supply pumps in the thermal power plant to increase the reliability of the induction motor operation and to reduce the costs of maintenance. Hardware and software configuration is explained in details as well as measuring results of the last year period.
The aim of this research was to discover the best indicators of induction motor faults, as well as suitable techniques for monitoring the condition of induction motors. Numerical magnetic field analysis was used with the objective of generating reliable virtual data to be analysed with modern signal processing and soft-computing techniques. In the first part of the research, a fuzzy system, based on the amplitudes of the motor current, was implemented for online detection of stator faults. Later on, from the simulation studies and using support vector machine (SVM), the electromagnetic force was shown to be the most reliable indicator of motor faults. Discrete wavelet transform (DWT) was applied to the stator current during the start-up transient, showing how the evolution of some frequency components allows the identification and discrimination of induction motor faults. Predictive filtering was applied to separate the harmonic components from the main current signal. The second part of the research was devoted to the development of a mechanical model to study the effects of electromagnetic force on the vibration pattern when the motor is working under fault conditions. The third part of this work, following the indications given by the second part, is concerned with a method that allows the prediction of the effect of the electromechanical faults in the force distribution and vibration pattern of the induction machines. The FEM computations show the existence of low-frequency and low-order force distributions acting on the stator of the electrical machine when it is working under an electrical fault. It is shown that these force components are able to produce forced vibration in the stator of the machine. This is corroborated by vibration measurements. These low-frequency components could constitute the primary indicator in a condition monitoring system. During the research, extensive measurements of current, flux and vibration were carried out in order to supply data for the research group. Various intentional faults, such as broken rotor bars, broken end ring, inter-turn short circuit, bearing and eccentricity failures, were created. A real dynamic eccentricity was also created. Moreover, different supply sources were used. The measurements supported the analytical and numerical results. TKK dissertations, ISSN 1795-4584; 85
This paper is intended as a tutorial overview of induction motors
signature analysis as a medium for fault detection. The purpose is to
introduce in a concise manner the fundamental theory, main results, and
practical applications of motor signature analysis for the detection and
the localization of abnormal electrical and mechanical conditions that
indicate, or may lead to, a failure of induction motors. The paper is
focused on the so-called motor current signature analysis (MCSA) which
utilizes the results of spectral analysis of the stator current. The
paper is purposefully written without “state of the art”
terminology for the benefit of practicing engineers in facilities today
who may not be familiar with signal processing
The paper discusses the application of finite element method in
the research of the magnetic field of induction motors having rotor cage
asymmetry. Several quantities (magnetic vector potential, flux density,
forces, torque, rotor and stator currents, mutual and leakage
inductances) can be determined very precisely. This enables the study of
increasing anomaly in magnetic field distribution in the cross-section
of induction motors due to increasing broken rotor bars which results in
a degradation of the steady-state and dynamic performance of induction
motors
The subject of online detection of airgap eccentricity in
three-phase induction motors is discussed, and a noninvasive approach,
based on the computer-aided monitoring of the stator current Park's
vector, is introduced. Experimental results, obtained by using a special
fault producing test rig, demonstrate the effectiveness of the proposed
technique, for detecting the presence of airgap eccentricity in
operating three-phase induction machines