scholarly journals Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction

2021 ◽  
Vol 11 (19) ◽  
pp. 9115
Author(s):  
Menshawy A. Mohamed ◽  
Mohamed A. Moustafa Hassan ◽  
Fahad Albalawi ◽  
Sherif S. M. Ghoneim ◽  
Ziad M. Ali ◽  
...  
Keyword(s):  
Induction Motor ◽  
Fuzzy Inference ◽  
Short Circuit ◽  
Experimental Tests ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Inference System ◽  
Stator Current ◽  
Set Up ◽  
Cage Induction Motor

This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.

scholarly journals Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8523
Author(s):  
Marcin Tomczyk ◽  
Ryszard Mielnik ◽  
Anna Plichta ◽  
Iwona Gołdasz ◽  
Maciej Sułowicz
Keyword(s):  
Genetic Algorithm ◽  
Induction Motor ◽  
Diagnostic Method ◽  
Induction Motors ◽  
Short Circuit ◽  
Experimental Tests ◽  
Stator Winding ◽  
Short Circuits ◽  
Cage Induction Motor ◽  
Industry Conditions

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

scholarly journals Combining Haar Wavelet and Karhunen Loeve Transforms for Medical Images Watermarking

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Mohamed Ali Hajjaji ◽  
El-Bay Bourennane ◽  
Abdessalem Ben Abdelali ◽  
Abdellatif Mtibaa
Keyword(s):  
Fuzzy Inference ◽  
Medical Images ◽  
Haar Wavelet ◽  
Discrete Wavelet ◽  
Correct Identification ◽  
Hidden Information ◽  
Inference System ◽  
Optimal Value ◽  
Karhunen Loeve Transform ◽  
Set Up

This paper presents a novel watermarking method, applied to the medical imaging domain, used to embed the patient’s data into the corresponding image or set of images used for the diagnosis. The main objective behind the proposed technique is to perform the watermarking of the medical images in such a way that the three main attributes of the hidden information (i.e., imperceptibility, robustness, and integration rate) can be jointly ameliorated as much as possible. These attributes determine the effectiveness of the watermark, resistance to external attacks, and increase the integration rate. In order to improve the robustness, a combination of the characteristics of Discrete Wavelet and Karhunen Loeve Transforms is proposed. The Karhunen Loeve Transform is applied on the subblocks (sized8×8) of the different wavelet coefficients (in the HL2, LH2, and HH2 subbands). In this manner, the watermark will be adapted according to the energy values of each of the Karhunen Loeve components, with the aim of ensuring a better watermark extraction under various types of attacks. For the correct identification of inserted data, the use of an Errors Correcting Code (ECC) mechanism is required for the check and, if possible, the correction of errors introduced into the inserted data. Concerning the enhancement of the imperceptibility factor, the main goal is to determine the optimal value of the visibility factor, which depends on several parameters of the DWT and the KLT transforms. As a first step, a Fuzzy Inference System (FIS) has been set up and then applied to determine an initial visibility factor value. Several features extracted from the Cooccurrence matrix are used as an input to the FIS and used to determine an initial visibility factor for each block; these values are subsequently reweighted in function of the eigenvalues extracted from each subblock. Regarding the integration rate, the previous works insert one bit per coefficient. In our proposal, the integration of the data to be hidden is 3 bits per coefficient so that we increase the integration rate by a factor of magnitude 3.

scholarly journals Thermal Study of Three-Phase Squirrel Cage Induction Motor with the Open-Phase Fault Operation Using a Lumped Parameter Network (LPTN)

2021 ◽  
Vol 23 (2) ◽  
pp. 87-94
Author(s):  
Mahdi Atig ◽  
Mustapha Bouheraoua ◽  
Rabah Khaldi
Keyword(s):  
Induction Motor ◽  
Convection Heat Transfer ◽  
Experimental Tests ◽  
Operating Conditions ◽  
Thermal Network ◽  
Squirrel Cage ◽  
Lumped Parameter ◽  
Squirrel Cage Induction Motor ◽  
Three Phase ◽  
Cage Induction Motor

The aim of this paper is to estimate the induction motor temperature at both steady and transient thermal states under healthy and faulty conditions. The distribution of the temperature in the motor is calculated using thermal models based on the 2D Lumped Parameter Thermal Network (LPTN). The thermal model takes into account the heat sources, convection heat transfer and the thermal resistances in the motor. The heat flow generated by the conduction and convection in a three-phase squirrel cage induction motor is discussed. The developed model is used to study the motor thermal behavior during the opening phase situation. The results obtained by the model developed are validated by experimental tests. The tested machine is a standard three-phase, 4-pole, 2.2 kW, 380 V squirrel cage induction motor of Totally Enclosed Fan Cooled “TEFC” design manufactured in Algeria by Electro-Industries company. The simulated temperatures so obtained are in good agreement with the measured ones, and the 2D Lumped Parameter Thermal Network study seems to be appropriate to characterize the heating of the active parts of the machine under different operating conditions.

scholarly journals Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 858 ◽  
Author(s):  
Sadeq D. Al-Majidi ◽  
Maysam F. Abbod ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Fuzzy Inference ◽  
Experimental Tests ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Training Data ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Inference System ◽  
Photovoltaic Array ◽  
Power Point

Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

scholarly journals The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 3009
Author(s):  
Pawel Ewert
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Induction Motor ◽  
Induction Motors ◽  
Frequency Converter ◽  
Experimental Tests ◽  
Test Mass ◽  
Mechanical Vibrations ◽  
Stator Current

This article presents the effectiveness of bispectrum analysis for the detection of the rotor unbalance of an induction motor supplied by the mains and a frequency converter. Two diagnostic signals were analyzed, as well as the stator current and mechanical vibrations of the tested motors. The experimental tests were realized for two low-power induction motors, with one and two pole pairs, respectively. The unbalance was modeled using a test mass mounted on a specially prepared disc and directly on the rotor and the influence of this unbalance location was tested and discussed. The results of the bispectrum analysis are compared with results of Fourier transform and the effectiveness of unbalance detection are discussed and compared. The influence of the registration time of the analyzed signal on the quality of fault symptom analyses using both transforms was also tested. It is shown that the bispectrum analysis provides an increased number of fault symptoms in comparison with the classical spectral analysis as well as it is not sensitive to a shorter registration time of the diagnostic signals.

scholarly journals An analysis of inter-bar currents on a polyphase cage induction motor

2004 ◽  
Vol 15 (4) ◽  
pp. 476-484
Author(s):  
Renato Carlson ◽  
Cláudia A. da Silva ◽  
Nelson Sadowski ◽  
Michel Lajoie-Mazenc
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Induction Motor ◽  
Induction Motors ◽  
Short Circuit ◽  
Fundamental Component ◽  
Cage Induction Motor ◽  
Element Method

This work uses a methodology based on 2D-Finite Element Method (FEM) and on the Circuits Theory (Independent Currents Method) to analyze the inter-bar currents on the rotor of cage induction motors. The Multi-Slice Technique is used to consider the skewing effect. Three conditions are considered: one inter-bar resistance, two inter-bar resistances and three inter-bar resistances. The results show the distribution of currents in the rotor bars, short-circuit rings and transversal resistances at a given time. The fundamental component of the inter-bar and surrounding bar currents are shown to help understanding the phenomenon.

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