Duygu BayramIstanbul Technical University · Department of Electrical Engineering
Duygu Bayram
Doctor of Engineering
About
29
Publications
1,159
Reads
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97
Citations
Citations since 2017
Introduction
Researcher with experience on technical design and dynamics for electric machines. Additionally signal processing applications and soft computing algorithms for condition monitoring and diagnostics purposes. Working on various signals, able to interpret the system with electric machinery knowledge.
Additional affiliations
February 2015 - present
Mekatro Research and Development
Position
- Senior Researcher
March 2007 - February 2015
Publications
Publications (29)
Deep learning methods are of utmost importance in the field of nanotechnology due to their practical applications provide insights into an optimal design of nanomaterials with multi-characteristics. In the context of present research, we propose fully connected deep neural network (DNN) classifiers which have the capability to classify light transm...
The present study addresses a comparative performance assessment of multivariate regression (MVR) and well-optimized feed-forward, generalized regression and radial basis function neural network models which aimed to predict transmitted light intensity (Itr) of carbon nanotube (CNT)-loaded polymer nanocomposite films by employing a large set of spe...
An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor ar...
In this paper, an electromagnetic analysis study of a double-sided linear permanent magnet (PM) motor topology is conducted both for conventional and Halbach array magnet assemblies. Halbach arrays can condense flux lines on the one side of magnet assembly and cancel them on the reverse side, so they enable light motor structures due to the lack of...
Due to the constant current demanding nature of LEDs, SMPS circuits are widely used as LED drivers. In this study a Ćuk converter for an LED lighting luminaire driving is designed and tested. To satisfy the constant current demand of the LED luminaire, an integrated circuit with current control capability is implemented. The LED current is sensed b...
This study aims at investigating different LED driver topologies used in lighting systems. In order to do this, the circuitry of AC/DC passive and switched-mode drivers will be analyzed, with special emphasis on the different components used in the circuits and their performances with regard to overall performance of the LED driver. The study aims...
In this study, a signal based predictive fault detection approach is developed to identify potential faults within an electric motor. In order to evaluate the performance of the proposed approach, first artificial motor vibration data is produced and used as a base line for analysis and assessment of the methodology. After successfully confirming p...
![Fig. 1 Schematic of the IRIS system [2]](profile/Duygu-Bayram-2/publication/306307792/figure/fig1/AS:595072819159040@1518887824595/Schematic-of-the-IRIS-system-2_Q320.jpg)
Small modular nuclear reactors (SMRs) are designed for long-term operation with minimum outages and for possible deployment in remote locations. To achieve this operational goal, the SMRs may require remote and continuous monitoring of performance parameters that contribute to operation and maintenance. This feature is also important in monitoring...
Dynamic fluctuations of nuclear plant sensors contain information about their response characteristics and bandwidth features. The random fluctuations can be characterized by using auto-regression (AR) time-series models. These discrete-time models are then utilized to estimate time-domain and frequency-domain signatures. Prior to developing these...
The characterization of the aging of electric motors using vibration measurements and a geometric interpretation of spectral domain signatures is presented. For comparison, two vibration signal records from a three-phase induction motor were acquired during degradation due to bearing fluting, following thermal and chemical aging sequences. Power sp...
The aging mechanism of an induction motor is observed using vibration signatures in this study. A wavelet based trending application is used through Multi Resolution Wavelet Analysis. The progress of aging is shown and interpreted based on the trends of different cases of the same induction motor. Aging region concept is introduced to designate a n...
In this study, the aging process of an electric motor is accomplished by adaptive neuro-fuzzy inference system (ANFIS) using vibration signals. Different ANFIS models are compared for representing the aging process in the best possible way. An artificial aging experiment is performed and vibration data taken from the initial (healthy) and final (fa...
This study presents a Wavelet based Neuro-Detector approach employed to detect the aging indications of an electric motor. Analysis of the aging indications, which can be seen in the low frequency region, is performed using vibration signals. More specifically, two vibration signals are observed for healthy and faulty (aged) cases which are measure...
Nonlinear systems like electrical circuits and systems, mechanics, optics and even incidents in nature may pass through various bifurcations and steady states like equilibrium point, periodic, quasi-periodic, chaotic states. Although chaotic phenomena are widely observed in physical systems, it can not be predicted because of the nature of the syst...
Projects
Projects (2)
The ultimate goal of this project is to develop shallow and deep neural networks, fuzzy logic, regression tree, support vector machine models and neuro-fuzzy systems trained with the most advanced optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), which yield considerably improved prediction capability better than conventional machine learning methods, for the multifunctional features of polymer nanocomposites.
