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Author

Muhammet Unal

Other affiliations: Gazi University
Bio: Muhammet Unal is an academic researcher from Marmara University. The author has contributed to research in topics: Structural health monitoring & Artificial neural network. The author has an hindex of 11, co-authored 25 publications receiving 490 citations. Previous affiliations of Muhammet Unal include Gazi University.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors proposed an artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results, every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value.

157 citations

Book ChapterDOI
01 Jan 2013
TL;DR: An Artificial Neural Network (ANN) as discussed by the authors is an information processing system that has certain performance characteristics in common with biological neural networks, such as it can be considered as threshold units that fire when their total input exceeds certain bias levels.
Abstract: An Artificial Neural Network is an information processing system that has certain performance characteristics in common with biological neural networks. Artificial neural networks have been developed as generalizations of mathematical models of human cognition or neural biology [16]. The basic processing elements of neural networks are called neurons. Neuron performs as summing and nonlinear mapping junctions. In some cases they can be considered as threshold units that fire when their total input exceeds certain bias levels. Neurons usually operate in parallel and are configured in regular architectures. The neurons are generally arranged in parallel to form layers. Strength of the each connection is expressed by a numerical value called a weight, which can be modified [17].

133 citations

Book
08 Sep 2012
TL;DR: This book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters.
Abstract: Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.

39 citations

Journal ArticleDOI
TL;DR: In this paper, the performance of the surface response to excitation (SuRE) method was evaluated with the conventional piezoelectric elements and scanning laser vibrometer used as contact and non-contact sensors, respectively, for monitoring the presence of loads on the surface.

37 citations

Journal ArticleDOI
TL;DR: In this paper, the location of applied load on an aluminum and a composite plate was identified using two type of neural network classifiers: Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) classifiers.

27 citations


Cited by
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Journal ArticleDOI
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.

1,173 citations

Journal ArticleDOI
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.

453 citations

Journal ArticleDOI
TL;DR: A novel deep architecture based bearing diagnosis method is proposed using cognitive computing theory, which introduces the advantages of image recognition and visual perception to bearing fault diagnosis by simulating the cognition process of the cerebral cortex.

308 citations

Journal ArticleDOI
TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.

257 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis.
Abstract: Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.

248 citations