Aşkar, Murat

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/7095
Name Variants
ASKAR, M
Askar, Murat
Askar, M
Askar, MA
Aşkar, M
Job Title
Email Address
murat.askar@ieu.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Current Staff
Website
ORCID ID
0000-0001-9244-3340
Scopus Author ID
7003498558
Turkish CoHE Profile ID
65D90CAD0480C06E
Google Scholar ID
OTMbIfcAAAAJ
WoS Researcher ID
E-7377-2017
Files
Mu-As.jpg (6.78 KB)

Sustainable Development Goals

8

DECENT WORK AND ECONOMIC GROWTH
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1

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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3

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10

REDUCED INEQUALITIES
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0

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17

PARTNERSHIPS FOR THE GOALS
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0

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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

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7

AFFORDABLE AND CLEAN ENERGY
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2

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1

NO POVERTY
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0

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5

GENDER EQUALITY
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0

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13

CLIMATE ACTION
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1

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4

QUALITY EDUCATION
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14

LIFE BELOW WATER
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0

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2

ZERO HUNGER
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0

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15

LIFE ON LAND
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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6

CLEAN WATER AND SANITATION
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3

GOOD HEALTH AND WELL-BEING
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1

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11

SUSTAINABLE CITIES AND COMMUNITIES
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Documents

40

Citations

1622

h-index

10

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Documents

32

Citations

1212

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Scholarly Output

13

Articles

5

Views / Downloads

19/99

Supervised MSc Theses

3

Supervised PhD Theses

0

WoS Citation Count

1133

Scopus Citation Count

1464

WoS h-index

5

Scopus h-index

5

Patents

0

Projects

0

WoS Citations per Publication

87.15

Scopus Citations per Publication

112.62

Open Access Source

3

Supervised Theses

3

JournalCount
2022 Medıcal Technologıes Congress (Tıptekno'22)1
2025 Symposium on Computational Intelligence on Engineering/Cyber Physical Systems-CIES -- MAR 17-20, 2025 -- Trondheim, NORWAY1
26th IEEE Signal Processing and Communications Applications Conference, SIU 20181
48th Conference of the Industrial Electronics Society-IECON-Annual -- Oct 17-20, 2022 -- Brussels, Belgium1
Advanced Science Letters1
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Now showing 1 - 10 of 11
  • Thumbnail Image
    Article
    Citation - WoS: 1056
    Citation - Scopus: 1346
    Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2016) İnce, Türker; Kiranyaz, Serkan; Eren, Levent; Askar, Murat; Gabbouj, Moncef
    Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
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    Conference Object
    Citation - Scopus: 1
    Improved Detection of Broken Rotor Bars by 1-D Self-Onns
    (IEEE, 2022) Eren, Levent; Devecioglu, Ozer Can; Ince, Turker; Askar, Murat
    Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.
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    Article
    Citation - WoS: 53
    Citation - Scopus: 65
    Enhanced Bearing Fault Detection Using Multichannel, Multilevel 1d Cnn Classifier
    (Springer, 2022) Ozcan, Ibrahim Halil; Devecioglu, Ozer Can; İnce, Türker; Eren, Levent; Askar, Murat
    Electric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Through early detection of bearing faults, it would be possible to solve the problem at a lower cost by repairing and/or replacing relevant parts. Most of the fault detection works in the literature attempted to detect binary {healthy, faulty} motor fault case based on a single input. In this study, we propose an enhanced performance bearing fault diagnosis system based on multichannel, multilevel 1D-CNN classifier processing vibration data collected from multiple accelerometers mounted on bearings in a test bed. Effectiveness and feasibility of the proposed method are validated by applying it to the benchmark IMS bearing vibration dataset for inner race and rolling element faults and comparing the results with the conventional single-axis data-based fault detection.
  • Thumbnail Image
    Article
    Citation - WoS: 9
    Citation - Scopus: 11
    Motor Current Signature Analysis Via Four-Channel Fir Filter Banks
    (Elsevier Sci Ltd, 2016) Eren, Levent; Askar, Murat; Devaney, Michael J.
    Motor current signature analysis (MCSA) is capable of providing continuous monitoring of induction motors in a non-intrusive manner. Fourier based techniques have been used widely in processing of stator current but these techniques have a shortcoming in processing non-stationary signals such as the stator current. Recently, wavelet packet decomposition (WPD) has become popular in such applications since it gives better results in the case of non-stationary signals. The latter approach has much higher computational complexity limiting its use in motor diagnostics applications. In this study, the use of four-channel FIR filter banks is proposed to provide lower computational complexity. Four-channel filter banks employ higher level of parallel processing than currently used two-channel filter banks. FPGA implementation of the proposed algorithm would result in even further reduction in overall computation time. (C) 2016 Elsevier Ltd. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 2
    A New Wave-Pipelining Methodology: Wave Component Sampling Method
    (Taylor & Francis Ltd, 2014) Sever, Refik; Askar, Murat
    In this article, a new wave-pipelining methodology named wave component sampling method, is proposed. In this method, only the component of a wave, whose maximum and minimum delay difference exceeds the tolerable value, is sampled, and the other components continue to propagate through the circuit. Therefore, the total number of registers required for synchronisation decreases significantly. For demonstrating the effectiveness of the proposed method, it is applied to 8 x 8 bit carry save adder multiplier using 90 nm CMOS technology. Monte Carlo and corner simulation results show that 8 x 8 bit multiplier can operate at a speed of 3.70 GHz, using only 70 latches. Comparing with the mesochronous pipelining scheme, the number of the registers is decreased by 41% and the total power consumption of the chip is also decreased by 8.3% without any performance loss.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 7
    Citation - Scopus: 13
    Fixed-Point Fpga Implementation of Ecg Classification Using Artificial Neural Network
    (IEEE, 2022) Dal, Barış; Askar, Murat
    Cardiovascular diseases (CVDs) are one of the major causes of mortality around the world. Hence, regular monitoring of electrocardiogram (ECG) signals is crucial for early diagnosis and treatment. This leads to the ASIC/FPGA implementation of ECG classification. The currently suggested FPGA developments depend on statistical analysis of ECG signals to extract some features as the input for the classification network. However, feature extraction methods may cause some information loss. Therefore, an Artificial Neural Network (ANN) model that takes raw input data has been proposed in this work. The MIT-BIH arrhythmia dataset is used for the training and validation of the model. The proposed architecture consists of 2 hidden layers and an output layer. The training achieves around 97% accuracy. The network parameters (weights and biases) are extracted from the trained model as 32-bit floating-point numbers and converted into fixed-point numbers (8-bit) for efficient mapping to the FPGA. Then, the mathematical model of the feed-forward network was developed on Xilinx Zybo FPGA using Verilog HDL. The whole procedure is completed in 232 clock cycles.
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    Conference Object
    Citation - WoS: 6
    Citation - Scopus: 23
    Heat Leakage Detection and Surveiallance Using Aerial Thermography Drone
    (Institute of Electrical and Electronics Engineers Inc., 2018) Kayan, Hakan; Eslampanah, Raheleh; Yeganli F.; Askar M.
    In recent years, UAVs provide an excellent investigative tool used for detecting heat leakages and their surveillance using high-resolution thermal cameras. In this work a low cost optimal aerial drone for surveillance and heat leakage detection is developed. The developed hexacopter have been used to take thermal images. These images have been analyzed by a developed image processing toolkit to find a solution for important needs in civil applications like search and rescue, surveillance and heat loss mapping for buildings. Our toolkit illustrates the heat leakages in the picture and calculates the total waste of money due to heat leakages of buildings. © 2018 IEEE.
  • Thumbnail Image
    Master Thesis
    Intelligent Solar Energy Toolkit
    (İzmir Ekonomi Üniversitesi, 2013) Toçoğlu, Mansur Alp; Aşkar, Murat
    Seçilmiş olan bir güneş panel tipinin belirlenmiş bir zaman aralığında üretebileceği enerjiyi tahmin eden bir yazılım programı geliştirilmiştir. Bu tahminleri yapabilmek için yapay sinir ağları kullanan bir model oluşturulmuştur. Bu modelin ihtiyaç duyduğu ham veriler Ankara'da kurulmuş olan bir güneş panel istasyonu ve yerel meteoroloji istasyonunun yanında Meteoroloji Genel Müdürlüğü'nden alınan ham veriler tarafından karşılanmıştır. Elde edilen bu ham veriler sırasıyla; üretilmiş olan güneş enerjisi, güneş radyasyonu, sıcaklık, nem, rüzgar hızı ve güneş paneli verimi değerleridir. Yapılmış olan bu yazılım programı aynı zamanda kurulacak olan bir güneş paneli istasyonunun maliyetinin muhtemel geri ödemeleri ile ilgili olarak kullanıcılara bir güneş enerjisi yatırım maliyeti tablosu sunmaktadır.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Thermal Image-Based Fault Diagnosis in Induction Machines Via Self-Organized Operational Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kilickaya, Sertac; Celebioglu, Cansu; Eren, Levent; Askar, Murat
    Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure. Mechanical faults such as misalignment and rotor issues are among the most common problems encountered in industrial environments. To effectively monitor and detect these faults, a variety of sensors, including accelerometers, current sensors, temperature sensors, and microphones, are employed in the field. As a non-contact alternative, thermal imaging offers a powerful monitoring solution by capturing temperature variations in machines with thermal cameras. In this study, we propose using 2dimensional Self-Organized Operational Neural Networks (SelfONNs) to diagnose misalignment and broken rotor faults from thermal images of squirrel-cage induction motors. We evaluate our approach by benchmarking its performance against widely used Convolutional Neural Networks (CNNs), including ResNet, EfficientNet, PP-LCNet, SEMNASNet, and MixNet, using a Workswell InfraRed Camera (WIC). Our results demonstrate that Self-ONNs, with their non-linear neurons and self-organizing capability, achieve diagnostic performance comparable to more complex CNN models while utilizing a shallower architecture with just three operational layers. Its streamlined architecture ensures high performance and is well-suited for deployment on edge devices, enabling its use also in more complex multi-function and/or multi-device monitoring systems.
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    Master Thesis
    Smart garden: Hydroponic LED garden /
    (İzmir Ekonomi Üniversitesi, 2020) Kalaycı, Mert; Aşkar, Murat
    Dünya nüfusu son 10 yılda yaklaşık 4 kat artmıştır. Küresel ısınmanın neden olduğu iklim değişikliği ise tarım faaliyetlerine ciddi manada zararlar vermektedir. Bu kapsamda 2050 yılında global çaplı su ve gıda krizi beklenmektedir. Smart Garden: Hydroponic LED Garden Projesi kapsamında Endüstri 4.0 ve LED Teknolojisi ile Topraksız tarım birleştirilerek hiçbir zirai ilaç kullanmadan kat çıkabilme imkanı ile birim alanı 30 kat daha verimli kullanan normal tarıma oranla %95 daha az su kullanarak 4 mevsim iklimsiz üretim yapan yeni nesil topraksız bitki üretim tesislerinin geliştirilmesi hedeflenmiştir.