Stator

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor (S/R) faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both S/R intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works.

In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed estimation and the other for the stator and rotor flux estimation. The input training data has been collected based on the currents and voltage data, while the output training data of the speed and stator and rotor fluxes has been established based on the measured speed and flux estimator-based mathematical model of the IM. The designed ANN estimators can overcome the problem of the parameter’s variations and drift integration problems. Matlab/Simulink has been used to develop and test the ANN estimators. The results prove the ANN estimators’ effectiveness under various operation conditions.

The purpose of this work is to study the aerodynamic performance of a tandem C4 base-profile compressor blade using numerical tools. In this paper, the flow along the tandem blade is studied for various relative blade positions. In all the studied cases, the front blade is fixed and the position of the rear blade is varied as a function of the axial and tangential displacements. A computer code was developed in "Visual Basic" using linear strength vortex-panel method to predict the aerodynamic performance of the tandem blade.

An important element in the National Aerospace Laboratory NLR prediction model for rotor-stator interaction noise is the unsteady lifting surface theory for a rotating blade row. To validate this lifting surface theory, a wind-tunnel experiment has been carried out using a through-flow nacelle with a hub, a 16-bladed rotor, and an 18-vane stator. Two mechanisms of sound generation were recognized. As usual in rotor-stator configurations, sound was produced by interaction of rotor viscous wakes with the downstream stator vanes. In addition, due to the small gap between rotor and stator, sound was generated by interaction of the stator vanes, with flow distortions caused by the thickness of the rotor blades and vice versa. Trip wires were attached to blades and vanes to simulate high-Reynolds-number flow. Unsteady pressures were measured on a stator vane, on a rotor blade, and in the inlet. Experiment and theory are briefly described, and results from both are compared. For most of the measured cases, the agreement is good.

Detection of eccentricity faults has been feasible using physical and vibrational parameters. The Brushless DC motor is undergoes various kinds of stresses including mechanical, electrical, thermal, and environmental. These stresses increase the probability of occurrences of stator faults with various severity levels. The well maintained BLDC motor and correct operations decrease the probability of occurrences of faults. These faults are of three types as damage in laminations, damage in frame of stator and faults in stator winding. As per study of IEEE and EPRI, most frequent fault is stator fault, which is approximately 28–36 % of overall faults [1, 2]. Majority of these faults are due to a combination of above stresses. It is essential to detect and diagnose such faults at the early stage through measurement and monitoring of changes in the magnetic flux of the BLDC motor in the motor windings and stator slots. In this paper, brushless dc motor and its stator, rotor and windings ...

ABSTRACT This paper proposes a stator flux oriented (SFO) control scheme of induction motors using variable-saturation regulators. The traditional SFO control scheme consists in the d-axis current component, torque, stator flux, and speed loops with four PI-type controllers. The proposed control scheme uses two regulators with variable-saturation in such a way that maximum dc-bus voltage utilization is reached and overcurrent problems are prevented. The experiments are carried out using dSpace digital controllers and show that the SFO control scheme, using variable-saturation regulators, guarantees the best dynamic performance in the field-weakening region with maximum dc-bus voltage utilization and without current overshoot.

Three-phase induction motors are the “workhorses” of industry; they are the most widely used electrical machine; because of its simple structure and high reliability. This paper proposes a new technique to model the stator winding of the induction motor in Matlab Simulink® software. This simulation of the induction motor would have the thermal behavior of its stator winding; to study the induction motor temperature estimation using motor parameter-based method. The modified model is used to validate a remote and Sensorless stator winding temperature estimation technique; therefore a thermal protection is obtained for soft-starter-connected to induction motors. The soft-starter is used to inject a DC signal in the induction motor terminal voltage and current. The stator winding resistance/temperature is estimated from DC signal injection by changing the gate drive signals of the Thyristor in the soft starter. The level of the injected DC signal is adjusted by the value of the delay a...

In this paper, a time stepping 2D and 3D FEM is performed for modeling and analysis interior rotor DFIG .The finite element method currently represents the state-of-the-art in the numerical magnetic field computation relating to electrical machines. FEM is a numerical method to solve the partial differential equations (PDE) that expresses the physical quantities of interest, in this case Maxwell's equations. This will result in a more accurate result compared to analytical modeling, which can be regarded as a simplification of the PDE. FEM analysis is used for transient mode, magnetic field calculation, the magnetic flux density and vector potential of machine is obtained. In this model we including, non linear material characteristics, eddy current effect, torque-speed characteristics, ambient temperature effect and magnetic analysis are investigated

A new method for the early detection of inter-turn faults in an induction motor is proposed in this paper. Simulation and experimental results shows that the proposed method works perfectly even under the dynamic load conditions of the induction motor. Reference frame transformation theory forms the backbone of the modeling of inter-turn fault of induction motor. The fault model has been prepared based on the synchronous reference frame. Mathematical model thus developed is then simulated using MATLAB/ SIMULINK®. Stator current signal were acquired using hall-effect sensor and NI Labview, which is further processed using Matlab software to obtain their wavelet coefficients up to 6 th level of decomposition. Statistical features of these wavelet coefficients were then extracted, analysed and it clearly indicates the interquartile range as a feature. A unique characteristic of the interquartile range of the 6 th level detailed wavelet coefficient obtained from the stator current is id...

This paper describes the optimal design for the geometry of a stator slot for use in high-speed spindle motor applications. The proposed method consists of the following three steps: first, choose the parameters of the stator slot that has a strong influence on the stator current, stator winding loss, iron loss, total loss, efficiency, and torque by using the analysis of the effects of stator slot geometry; second, create factors and levels in the Taguchi method to obtain the optimal combination of the stator slot parameters from the analysis effect of the parameter results; third, using Genetic Algorithms (GAs) to determine the optimal value from the optimal combination of the results of the Taguchi method. Optimal design and performance analysis was performed using the Finite element Method (FEM) and verification by using equivalent circuit analysis. The optimization results were evaluated by comparing them with original performance. According to the test results and analysis, the...