Profile URL: https://hdl.handle.net/20.500.13091/13470
Job Title:Doç. Dr.
Email Address:mmutluer@ktun.edu.tr
Main Affiliation:02.04. Department of Electrical and Electronics Engineering
Status: Current Staff
ORCID:
0000-0002-6781-8937
0000-0002-6781-8937YÖK Akademik: C38D84CD950CD4B7
Google Scholar:
JPR8s4sAAAAJ
JPR8s4sAAAAJWeb of Science ID:
AAI-2172-2020
AAI-2172-2020Name Variants:
Mutluer, Mumtaz Mutluer, M.
3 results
Scholarly Output Search Results
Now showing 1 - 3 of 3
Article Experimental Comparative Evaluation of Machine Learning Methods for Early Multi-Fault Detection in Brushless DC Motors(MDPI, 2026-03-24) Sen, Mehmet; Mutluer, MumtazEarly and reliable fault detection in Brushless Direct Current (BLDC) motors is essential for improving system reliability and reducing unplanned industrial downtime. This study presents a controlled experimental investigation of data-driven machine learning approaches for the classification of multiple common BLDC motor faults. Four representative fault-related indicators were obtained under systematically designed operating conditions, and a consistent feature extraction procedure was applied prior to model development. A comparative evaluation was conducted using Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), k-Nearest Neighbour (kNN), and decision tree-based classifiers. All models were trained and tested on the same dataset using an identical validation protocol to ensure methodological fairness and reproducibility. Performance was assessed through standard classification metrics, enabling a transparent comparison of predictive capability and stability. The results show that the MLP model achieved the highest overall classification accuracy (91.6%), closely followed by SVM (91.4%) and kNN (90.2%). Although the performance differences are moderate, the neural network demonstrated more consistent behaviour in scenarios where fault signatures exhibited overlapping characteristics. These findings suggest that non-linear feature interactions play a significant role in BLDC fault discrimination and can be effectively captured by multi-layer architectures. The study provides a reproducible experimental framework and a balanced performance assessment that may support both academic research and the practical development of intelligent condition monitoring systems for BLDC-driven applications.Article Analysis and Design of a Permanent Magnet Linear Synchronous Motor Based on Inductance Calculation(Polska Akad Nauk, Polish Acad Sciences, 2025-08-22) Yucel, Enes; Mutluer, Mumtaz; Cunkas, MehmetThis paper presents a comprehensive design and analysis methodology for a Permanent Magnet Linear Synchronous Motor (PMLSM), with a focus on evaluating different inductance modeling approaches. The motor design begins with analytical dimensioning based on defined design parameters. A two-dimensional finite element analysis follows this in ANSYS Maxwell to verify magnetic saturation, back-EMF, flux linkage, and electromagnetic performance under full load conditions. The inductance parameters are calculated using both conventional and look-up table (LUT) based models. In the conventional model, seven different methods are tested under static and dynamic conditions, as well as in non-salient and salient scenarios, and their results are compared. In the LUT model, current-dependent inductance values are extracted from flux linkage maps. The motor designed in Maxwell, along with the calculated inductance data, is integrated into a dynamic cooperative simulation (co-sim) model controlled by an inverter in Simplorer to analyze the thrust force. The results show that the LUT model provides outputs that are closer to the co-sim reference than the traditional model. Furthermore, performance curves based on the Maximum Torque Per Ampere strategy are generated, and the force-speed and power-speed characteristics derived from both inductance models are compared. The findings emphasize the importance of accurate inductance modeling in capturing the actual electromagnetic behaviour of PMLSM under dynamic operating conditions.Article A Review of BLDC Motors: Types, Application, Failure Modes and Detection(MDPI, 2025-12-08) Sen, Mehmet; Mutluer, MumtazBrushless DC (BLDC) motors are widely used in many engineering fields such as transportation, industrial automation, pumping systems, household devices, and renewable energy applications. Their popularity mainly arises from advantages like high power density, low noise, long service life, and high efficiency. This study contributes to the literature by comprehensively addressing the types, applications, faults, and diagnostic methods of BLDC motors. This review systematically examines recent studies to identify and classify common mechanical, electrical, magnetic, thermal, and sensor-related faults. Diagnostic approaches reported in these studies are then analyzed and compared. The methods are grouped into several categories, including signal processing, model-based, data driven, artificial intelligence-supported, and thermal or magnetic monitoring techniques. The review results show that hybrid and intelligent diagnostic strategies, which combine different analysis methods, significantly improve the accuracy of fault detection and enable earlier fault identification. These improvements also contribute to higher reliability and safer operation of BLDC systems. In the discussion, attention is given to the growing use of artificial intelligence and data fusion in fault diagnosis. These trends are likely to guide the next generation of condition monitoring systems for BLDC motors. Overall, this study emphasizes the importance of developing reliable and sustainable diagnostic frameworks to enhance energy efficiency and system performance. The results can provide a useful reference for researchers and engineers working on BLDC motor technologies.
Research Topics
Domains
Physical Sciences
Fields
EngineeringMaterials Science
Subfields
Electrical and Electronic EngineeringMechanical EngineeringControl and Systems EngineeringElectronic, Optical and Magnetic Materials
Specific Research Areas
Electric Motor Design and Analysis
Sensorless Control of Electric Motors
Induction Heating and Inverter Technology
Magnetic Bearings and Levitation Dynamics
Magnetic Properties and Applications
Sustainable Development Goals
7AFFORDABLE AND CLEAN ENERGY
1
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
This researcher does not have a Scopus ID.
Documents
12
Citations
68
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Necmettin Erbakan University | 12 |
| Selçuk University | 9 |
| Konya Technical University | 3 |
| Central Statistical Office | 1 |
| Ankara University | 1 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Archives of Electrical Engineering | 1 |
| Energies | 1 |
| Eng | 1 |
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Scholarly Output
3
Articles
3
Views / Downloads
3/0
Supervised MSc Theses
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Supervised PhD Theses
0
WoS Citation Count
0
Scopus Citation Count
0
Patents
0
Projects
0
WoS Citations per Publication
0.00
Scopus Citations per Publication
0.00
Open Access Source
3
Supervised Theses
0
Scopus Quartile Distribution
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