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86 results on '"Kia, Shahin Hedayati"'

1. A comparative study for stator winding inter-turn short-circuit fault detection based on harmonic analysis of induction machine signatures

Author

Zorig, Assam, Kia, Shahin Hedayati, Chouder, Aissa, Rabhi, Abdelhamid, Modélisation, Information et Systèmes - UR UPJV 4290 (MIS), Université de Picardie Jules Verne (UPJV), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Numerical Analysis, General Computer Science, Applied Mathematics, Modeling and Simulation, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Theoretical Computer Science
Abstract

International audience; This article deals with inter-turn short-circuit fault detection in stator windings of squirrel cage induction machines. The main aim is to perform harmonic analysis of different electrical signatures namely the stator phase current, external magnetic flux and electromagnetic torque at different levels of mechanical load in order to develop an efficient fault detection approach of this kind of defect in induction machines. The proposed approach is based on the analysis of saturation related harmonics at rank 3k(1) f(s), where k(1) is an odd number, and magnetomotive force (MMF)-related harmonics at rank (6k(2) +/- 1) f(s), with k(2) = 1, 2, ... in stator phase current and stray flux and harmonics at rank 2k(2) f(s) in electromagnetic torque. The amplitudes of these last harmonics in healthy condition are compared with 3% power supply unbalance, 16.6% (40 turns) and 33% (80 turns) levels of inter-turn short-circuit fault in frequency range from 0 Hz to 2500 Hz under different levels of mechanical load. Besides, the stand-still test is also investigated in this work. Simulation study is carried out based on 2.2kW squirrel cage IM using finite element method (FEM). This method provides accurate and inexpensive tool for evaluating the performance of induction machine under healthy and faulty conditions. The obtained results demonstrate that the stray flux is the most sensitive signature to the stator winding inter-turn short-circuit fault, and it is robust against the power supply unbalance in comparison with both stator current and electromagnetic torque. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Published
2022

5. Torsional vibration effects on induction machine current and torque signatures in gearbox-based electromechanical system

Author

Kia, Shahin Hedayati, Henao, Humberto, and Capolino, Gerard-Andre

Subjects
Fatigue testing machines -- Measurement, Vibration -- Measurement, Induction electric motors -- Design and construction, Induction electric motors -- Mechanical properties, System design -- Methods, Systems analysis -- Methods, Materials -- Fatigue, Materials -- Measurement, System design, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

6. Analytical and experimental study of gearbox mechanical effect on the induction machine stator current signature

Author

Kia, Shahin Hedayati, Henao, Humberto, and Capolino, Gerard-Andre

Subjects
Induction electric motors -- Analysis, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The aim of this paper is the analytical and experimental study of a gearbox by using the stator current signature analysis in the driving induction machines. The mechanical study using the vibration signal analysis has been well studied before. Recently, some works have been performed to observe the vibration components of the mechanical part in the stator current spectrum for an induction machine but without any theoretical development. This paper proposes a theoretical framework based on torque oscillations due to the characteristic torsional vibrations in a gearbox, and the source of mesh and rotating frequency components in the stator current is presented. To verify the theoretical development, a test bed based on a 5.5-kW three-phase squirrel-cage machine connected to a gearbox has been used. Index Terms--Asynchronous rotating machines, current measurement, induction motors, mechanical systems, monitoring, phase modulation, signal processing, vibration measurement.

Published
2009

7. Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation

Author

Kia, Shahin Hedayati, Henao, Humberto, and Capolino, Gerard-Andre

Subjects
Machinery -- Analysis, Magneto-electric machines -- Analysis, Induction electric motors -- Analysis, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The aim of this paper is to present a wavelet-based method for broken-bar detection in squirrel-cage induction machines. The frequency-domain methods, which are commonly used, need speed information or accurate slip estimation for frequency-component localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any squirrel-cage induction machine due to numerous previous investigations. The proposed approach consists in the energy evaluation of a known bandwidth with time-scale analysis using the discrete wavelet transform. This new technique has been applied to the stator-current space-vector magnitude and the instantaneous magnitude of the stator-current signal for different broken-bar fault severities and load levels. Index Terms--AC motor protection, amplitude modulation (AM), asynchronous rotating machines, envelope detection, fault diagnosis, Hilbert transforms, induction motors, monitoring, signal processing, wavelet transforms.

Published
2009

9. A high-resolution frequency estimation method for three-phase induction machine fault detection

Author

Kia, Shahin Hedayati, Henao, Humberto, and Capolino, Gerard-Andre

Subjects
Algorithms -- Usage, Fourier transformations -- Analysis, Algorithm, Business, Computers, Electronics, Electronics and electrical industries
Abstract

Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition. Index Terms--Electric variables measurement, fault diagnosis, Fourier transforms, frequency estimation, induction machines, multidimensional signal processing, pattern classification.

Published
2007

10. Feature Learning Using Deep Neural Networks for Fault Diagnosis in Electromechanical Systems

Author

Heydarzadeh, Mehrdad, Mohammadi, Alireza, Kia, Shahin Hedayati, Nourani, Mehrdad, Henao, Humberto, Capolino, Gérard-André, Modélisation, Information et Systèmes - UR UPJV 4290 (MIS), and Université de Picardie Jules Verne (UPJV)

Subjects
[SPI.NRJ]Engineering Sciences [physics]/Electric power, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

11. Planetary Gear Tooth Fault Detection using Stator Current Space Vector Analysis in Induction Machine-Based Systems

Author

El Hajjaji, Ahmed, Kia, Shahin Hedayati, Marzebali, Mohammad Hoseintabar, Modélisation, Information et Systèmes - UR UPJV 4290 (MIS), and Université de Picardie Jules Verne (UPJV)

Subjects
Computer science, Stator, 020209 energy, Computation, 020208 electrical & electronic engineering, Drivetrain, 02 engineering and technology, Fault (power engineering), Transfer function, Fault detection and isolation, Wound rotor motor, law.invention, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Mechanical system, Control theory, law, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS
Abstract

The aim of this article is to present the planetary gear tooth fault detection using stator current space vector analysis in steady-state working condition of induction machine-based systems. For this study a universal method is used. The proposed technique is mainly relied on the computation of a transfer function which defines the response of the stator current space vector to the perturbations induced by gear tooth fault of the planetary gear located within the drive train. The idea of electrical-mechanical analogy is employed to facilitate the computation of the mentioned transfer function. This approach is considered as an upstream phase for studying the feasibility of gear tooth fault diagnosis using non-invasive measurement. A fault index is recommended for the fault severity evaluation at different levels of the mechanical load. The performance of the proposed fault index is validated through experiments using a representative induction machine-based test rig which includes a three-phase 5.5kW wound rotor induction machine connected to a fixed ring single-stage planetary gearbox.

Published
2019

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