Noor Mandeel Agag

Year: 2

Academic Degree: Master

Supervisor Type: Co-supervisor

Supervisor State: In Progress

Effect of vibration on natural convection of heat transfer

B.Sc. In Mechanical Engineering

May 4, 1992 - Aug 4, 1997

B.Sc. In Mechanical Engineering College
of Engineering-Mosul University/Iraq

M.Sc. in Mechanical Engineering

Aug 31, 1999 - Feb 5, 2002

M.Sc. in Mechanical Engineering/Applied Mechanics
College of Engineering-Mosul/Iraq

Ph.D.

Mar 2, 2011 - Dec 13, 2016

Ph.D. in Machine Condition Monitoring
UNIVERSITI TEKNOLOGI MALAYSIA/Malaysia

Mechanical Engineering [Senior Mechanical Engineering]

Jun 6, 2000 - Sep 19, 2002

Senior Mechanical Engineering in Iben Sini Hospital, Mosul. Jun 2000 – September 2002.

Managing and design ,preventive maintenance [Managing and design applications of preventive maintenance projects]

Oct 15, 2002 - Feb 16, 2010

Managing and design applications of preventive maintenance projects for the factories of Northern Cement Company, Mosul. October 2002 – February 2010.

Effect of sugar palm fibers on the properties of blended wheat starch/polyvinyl alcohol (PVA) -based biocomposite films

Jun 7, 2023

Journal Journal of Materials Research and Technology

publisher Elsevier Editora Ltda

DOI https://doi.org/10.1016/j.jmrt.2023.02.027

Issue may

Volume 24

Sugar palm fiber has been added to reinforce starch, polyvinyl alcohol (PVA) based film. The effect of reinforcement on different properties has been studied. It has been found that reinforcing plasticized starch/PVA matrix with palm fibers has considerably enhanced physical properties, the density of the polymer declined to 1.21 g/cm³ with untreated fibers and to 1.32 g/cm³ with treated fibers, which created a lighter weight bioplastic, and a reduction occurred in water absorption (for example 3% of treated fiber showed 143% absorbed water after 30 min immersing in water) and water solubility. When compared to the films without fibers filler, films reinforced with fibers demonstrated a c3onsiderable improvement in the crystal profile; at 9% fiber load, it improved over double. Additionally, it has been noted that thermal stability has increased. The existence of treated and untreated fibers in the hybrid matrix revealed 23.3% and 24.6% mass residues at 495 °C, respectively. However, this enhancement did not coincide with a rise in mechanical properties, Whilst, improvements in tensile strength and modulus occurred at 9% of treated fiber load, showing 12 MPa and 245 MPa, respectively. The highest elongation was 66.3% at 3% of treated fiber films. Meanwhile, films reinforced with treated sugar palm fibers showed higher mechanical properties than films with untreated sugar palm fibers. Scanning Electronic Microscope images exhibited higher interfacial interaction at 9% for both treated and untreated sugar palm fibers.

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Effect of the Cross-Sectional Shape on the Dynamic Response of a Cantilever Steel Beam Using Three Modal Analysis Methods

Mar 28, 2023

Journal Mathematical Modelling of Engineering Problems

publisher International Information and Engineering Technology Association (IIETA)

DOI https://doi.org/10.18280/mmep.110310

Issue 3

Volume 11

The main method for determining the vibration characteristics of engineering constructions is modal analysis. It's a way of analyzing a system's mode shapes, natural frequencies, and damping factor. The dynamic response of cantilever beams is determined in this work with different cross-sectional shapes to find the effect of eccentricity on the dynamic response of the cantilever beam. The main goal of this research is to find and detect the natural frequencies and mode shapes of a Structural Steel cantilever beam with different eccentricities and to identify flexural or torsional natural frequencies, as well as their mode shapes that could be confused with transverse natural frequencies, and to compare the results with analytical and experimental methodologies. Results showed that torsional natural frequencies remained within the transverse natural frequency. It can be shown that, the increase of eccentricity in the cross section decreases the natural frequencies and especially the torsional natural frequencies. The results were compared experimentally and numerically using ANSYS 16.1 software. There is a strong link between the mathematical, FEA, and experimental results. The latest results can be used to calculate failure loads in a variety of situations. The mathematical application of Euler's Bernoulli's beam concept was applied. The results of the three ways have been declared satisfactory.

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A review in particle image velocimetry techniques (developments and applications)

Feb 4, 2020

Journal Journal of Advanced Research in Fluid Mechanics and Thermal Sciences

publisher Semarak Ilmu Publishing

DOI https://www.akademiabaru.com/doc/ARFMTSV65_N2_P213_229.pdf

Issue 2

Volume 65

The latest entrant into the fluid flow measurement field is the particle image velocimetry (PIV) which offers velocity field immediately in flow domains. Referring to the definition, the placement is recorded by PIV over time pertaining to small tracer particles that were released in the flow for local fluid velocity extraction. Thus, PIV can be regarded as a quantitative extension pertaining to visualisation techniques for qualitative flow being practiced for a number of decades. This review provides a detailed background pertaining to evolution of PIV, principle of operation, basic elements, key features, uncertainty, errors in PIV as well as few applications of PIV. Recent advances pertaining to the PIV technique have been aimed at procuring all three components with regards to fluid velocity vectors simultaneously in a volume or in a plane that enables wider applications with the PIV technique for investigating more complex flow phenomena. In recent years, developing of various advanced PIV techniques have been successfully achieved, including three-dimensional (3D) particle-tracking velocimetry (3D-PTV), tomographic PIV, holographic PIV (HPIV) technique and stereo PIV (SPIV). A comparison has been done between the main PIV techniques. © 2020 Penerbit Akademia Baru.

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Using K-fold cross validation proposed models for SpikeProp learning enhancements

Oct 2, 2018

Journal International Journal of Engineering and Technology(UAE)

publisher Science Publishing Corporation Inc

DOI https://www.sciencepubco.com/index.php/ijet/article/view/20790

Issue 4

Volume 7

Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte's algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets. © 2018 Authors.

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Regrassion Modeling for Spur Gear Condition Monitoring Through Oil Film Thickness Based on Acoustic Emission Signal

Jul 6, 2015

Journal Modern Applied Science

publisher Canadian Center of Science and Education

DOI http://dx.doi.org/10.5539/mas.v9n8P21

Issue 8

Volume 9

The main purpose of a gear lubricant is to provide adequate oil film thickness to reduce and prevent gear tooth surface failures .Until now, there is no study in the literature related to the estimation of oil film thickness through Acoustic emission signals. In this study, for spur gear condition monitoring a new approach based on mathematical model was presented for oil film regimes detection. This study is focused on the ability of regression model to find whether the gearbox is running in elastohydrodynamic, mixed wear or severe wear lubrication mode. Then, forecasting accuracy of the model is measured by examine the prediction error that produced by using Mean Squared Error and Mean Absolute Error. In this paper a mathematical model for time-series prediction was considered and the results shows the ability of the regression model to predict oil film regime.

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Artificial neural network model for monitoring oil film regime in spur gear based on acoustic emission data

Mar 4, 2015

Journal Shock and Vibration

publisher Wiley’s

DOI https://doi.org/10.1155/2015/106945

Issue 2015

Volume 2015

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network. © 2015 Yasir Hassan Ali et al.

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ACOUSTIC EMISSION TECHNIQUE IN CONDITION MONITORINGAND FAULT DIAGNOSIS OF GEARS AND BEARINGS

Sep 16, 2014

Journal INTERNATIONALE JOURNAL of ACADEMIC RESEARCH

publisher Library of Congress Classification:

DOI DOI: 10.7813/2075-4124.2014/6-5/A.19

Issue 5

Volume 6

Transmission tools and rolling element bearings are recognized as very significant components in inmechanical equipment. Machine condition monitoring (CM) and fault diagnosis are therefore key elements in maintenance programs for these components. The acoustic emission (AE) technique is a successful method to evaluate the condition of machinery and to diagnose and monitor faults due to its sensitivity to identify microcracks apparent in the high-frequency domain. This paper presents a review of the literature on condition monitoring and fault diagnosis in gears and bearings using acoustic emission signals. It attempts to summarize recent research and developments. Although several similar studies have already been conducted, this places emphasis on the latest developments, and limits its scope to the application of acoustic emission on gears bearings (PDF) Acoustic emission technique in condition monitoring and fault diagnosis of gears and bearings. Available from: https://www.researchgate.net/publication/313601291_Acoustic_emission_technique_in_condition_monitoring_and_fault_diagnosis_of_gears_and_bearings [accessed Apr 05 2025].

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Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: A review

Jun 20, 2014

Journal Jurnal Teknologi

publisher UTM

DOI http://dx.doi.org/10.11113/jt.v69.3121

Issue 2

Volume 69

Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature. © 2014 Penerbit UTM Press. All rights reserved.

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الانجازات البحثية الحديثة في العلوم الانسانية

Aug 2, 2023 - Aug 3, 2023

Publisher جامعة شيراز

Country ايران

Location شسراز

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قراءات معرفية في العلوم الانسانية و الاجتماعية

May 27, 2023 - May 28, 2023

Publisher الكلية التقنية الجنوبية

Country Iraq

Location ذي قار

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Novel Spiking Neural Network Model for Gear Fault Diagnosis

Oct 25, 2022 - Oct 26, 2022

Publisher IEEE

DOI https://ieeexplore.ieee.org/document/9935414/

Country Yemen

Location Ibb, Yemen

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Fault Detection of Bearing using Support Vector Machine-SVM

Aug 24, 2020 - Aug 26, 2020

Publisher IEEE

DOI https://doi.org/10.1109/ICIMU49871.2020.9243507

Country MALAYSIA

Location Selangor, Malaysia

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Elevator Exhaustion Time Reduction by Eliminating Fake Demands

Jun 28, 2020 - Jun 30, 2020

Publisher EAI

DOI https://eudl.eu/doi/10.4108/eai.28-6-2020.2298156

Country Iraq

Location

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Diagnosis model for bearing faults in rotating machinery by using vibration signals and binary logistic regression

Dec 12, 2019 - Dec 13, 2019

Publisher AIP

DOI http://dx.doi.org/10.1088/1757-899X/328/1/012032

Country Indonesia

Location Jakarta, Indonesia

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Challenges Facing the Application of Total Quality Management in Northern Technical University

Mar 27, 2019 - Mar 28, 2019

Publisher جامعة الكوفة

Country Iraq

Location الكوفة

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Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

Dec 3, 2018 - Dec 5, 2018

Publisher MATEC Web Conf

DOI https://www.matec-conferences.org/articles/matecconf/abs/2019/04/matecconf_eaaic2018_02013/matecconf_eaaic2018_02013.html

Country MALAYSIA

Location Kota Kinabalu, Malaysia

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Adapted Wavelet Transform for Twisted Blade Diagnosis in Multi Stage Rotor

Dec 3, 2018 - Dec 5, 2018

Publisher MATEC Web Conf

DOI https://www.matec-conferences.org/articles/matecconf/abs/2019/04/matecconf_eaaic2018_02011/matecconf_eaaic2018_02011.html

Country MALAYSIA

Location Kota Kinabalu, Malaysia

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A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

Nov 22, 2017 - Nov 23, 2017

Publisher IOP

DOI http://dx.doi.org/10.1088/1757-899X/328/1/012032

Country MALAYSIA

Location Batu Ferringhi , Penang , Malaysia

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Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine – A Review,

Sep 24, 2016 - Sep 25, 2016

Country MALAYSIA

Location Pekan, MALAYSIA

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Condition Monitoring and Fault Diagnosis of Rotating Machine Based on Acoustic Emission and Artificial Intelligence Techniques

Sep 13, 2014 - Sep 14, 2014

Country Indonesia

Location Surabaya, Indonesia

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