Abstract:SUMMARY
Different radial eccentricities in salient pole machine are well documented in previous studies. In reality, the most probable case is the rotor eccentricity, which is not uniform down the axial length. This article determines a precise model of salient pole synchronous machine for general axial eccentricity condition. Proposed model allows calculating salient pole machine inductances with different static, dynamic, and mixed axial eccentricities in a unified technique. By taking into account machine g… Show more
“…The WFM is mostly adopted for a healthy machines whose air gap is symmetrical or uniform [3,15]. The MWFM is largely adopted for faulty machines since it accounts for air gap eccentricity [5,17,18]. The work of [29] revealed that Modified Winding Function Theory (MWFTh) is generally accepted in analyzing rotating electrical machines which have a very small air gap.…”
Section: Introductionmentioning
confidence: 99%
“…Majority of the researchers who investigated faulty machines patronized this method. The calculations of inductances of salient-pole synchronous motor operating under eccentricity using MWFTh were done by [17,20] and [21]. Again [21] developed an analytical model that can compute the machine inductances under any kind of eccentricity.…”
Fault diagnosis and condition monitoring of synchronous machines running under load is a key determinant of their lifespan and performance. Faults such as broken rotor bars, bent shafts and bearing issues lead to eccentricity faults. These faults if not monitored may lead to repair, replacement and unforeseen loss of income. Researchers who attempted to investigate this kind of machine stopped at characterizing and deduced ways, types and effects of rotor eccentricity fault on the machine inductances using the winding function method. A modified closed-form analytical model of an eccentric synchronus reluctance motor (SynRM) is developed here taking into cognizance the machine dimensions and winding distribution for the cases of a healthy and unhealthy SynRM. This paper reports the study the SynRM under static rotor eccentricity using the developed analytical model and firming up the model with finite element method (FEM) solutions. These methods are beneficial as they investigated and presented the influence of the degrees of static eccentricity on the machine performance indicators such as speed, torque and the stator
“…The WFM is mostly adopted for a healthy machines whose air gap is symmetrical or uniform [3,15]. The MWFM is largely adopted for faulty machines since it accounts for air gap eccentricity [5,17,18]. The work of [29] revealed that Modified Winding Function Theory (MWFTh) is generally accepted in analyzing rotating electrical machines which have a very small air gap.…”
Section: Introductionmentioning
confidence: 99%
“…Majority of the researchers who investigated faulty machines patronized this method. The calculations of inductances of salient-pole synchronous motor operating under eccentricity using MWFTh were done by [17,20] and [21]. Again [21] developed an analytical model that can compute the machine inductances under any kind of eccentricity.…”
Fault diagnosis and condition monitoring of synchronous machines running under load is a key determinant of their lifespan and performance. Faults such as broken rotor bars, bent shafts and bearing issues lead to eccentricity faults. These faults if not monitored may lead to repair, replacement and unforeseen loss of income. Researchers who attempted to investigate this kind of machine stopped at characterizing and deduced ways, types and effects of rotor eccentricity fault on the machine inductances using the winding function method. A modified closed-form analytical model of an eccentric synchronus reluctance motor (SynRM) is developed here taking into cognizance the machine dimensions and winding distribution for the cases of a healthy and unhealthy SynRM. This paper reports the study the SynRM under static rotor eccentricity using the developed analytical model and firming up the model with finite element method (FEM) solutions. These methods are beneficial as they investigated and presented the influence of the degrees of static eccentricity on the machine performance indicators such as speed, torque and the stator
“…Detection of the eccentricity fault in induction machines and synchronous generators has been the subject of many papers. New machines and drives need more precise, quick, and easy fault diagnosis methods, so mechanical faults such as eccentricity fault and magnetic faults such as demagnetization fault in permanent‐magnet (PM) machines have been widely studied in order to find more accurate and simpler diagnosis methods.…”
Summary
This paper presents a new analytical technique for modelling the impact of static eccentricity (SE), dynamic eccentricity (DE), and mixed eccentricity (ME) faults on electromotive force (EMF) or Back‐EMF, output voltages and currents of surface‐mounted permanent‐magnet (SMPM) machines. This model is based on the combination of the complex relative permeance and the eccentricity relative air‐gap permeance. It presents explicit formulas for no‐load and on‐load, field distribution, magnetic flux, EMF/Back‐EMF, and output voltages and currents of SMPM for different types of eccentricity fault. Furthermore, a new eccentricity model is presented and applied, which can cover more eccentricity fault types; some of them have not been so far considered in the literatures. Analytical results are validated by time‐stepping finite element method results. The analytical and numerical results refer to the fact that the SE does not cause any new frequency component in the EMF/Back‐EMF and currents spectra. However, the same is true in the case of DE and ME faults where some frequency components appear in the EMF/Back‐EMF and currents spectra, not only around the fundamental harmonic but also around some other harmonics.
“…The introduced approach uses standard statistical analyses like mean, variance, and information entropy, on one phase of the steady-state current signal provided by the VSD, together with sensorless synchronous speed estimation from the stator voltage supply, as inputs to an ANN for classifying the induction motor operational condition, different from reviewed literature, where a direct connection of the motor to the power supply is considered for simplicity and improved certainty of the used approach during the fault detection process. The introduced technique is able to detect distinct faults like BRB, misalignment (MAL), and unbalance (UNB) at different rotating speed, unlike previous approaches that detect just one single fault as bearings [6][7][8], rotor bars [9][10][11][12][13], and UNB [14][15][16][17][18] at a fixed rotating speed. Previous methods in reviewed literature use computationally complex signal processing techniques like WT [19][20][21][22][23], FT [24][25][26][27], and their combination with other techniques [28][29][30][31][32][33][34][35][36][37] for detecting multiple faults, which restrain them to off-line applications.…”
Section: Introductionmentioning
confidence: 99%
“…Differently, in , unbalance in induction generators is detected by using the ratio of active power to apparent power (power factor) as a condition monitoring criterion. The effects of axial eccentricities, as rotor asymmetries, on the machine inductances are studied in . The effects on rotor vibrations of design, manufacturing, and external factors, like mechanical unbalance, are presented and discussed in .…”
SUMMARYInduction motors are key elements of every industrial process. A faulty motor produces interruptions on production lines, with consequences in cost, product quality, and safety. The relevance in induction motor monitoring is the ability to detect faults in incipient state. Many proposed methods consider direct connection of motors to the power supply; however, the common practice in industry is to connect them through variable speed drives (VSD), which introduce harmonics into the current supply signal that make the fault identification extremely difficult. This work proposes a statistical analysis through mean, variance, and information entropy computation, combined with sensorless rotating speed estimation for classifying different faults in induction motors using an artificial neural network. The proposed methodology examines the voltage and current signals provided by an industrial VSD that ensures a high certainty on identifying the treated faults at different rotational speed. A field programmable gate array-based implementation is developed to offer an online, system-on-chip solution for real-time condition monitoring.
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