Kılıçkaya, Sertaç
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S Kılıçkaya
S Kilickaya
Sertac, Kilickaya
Kilickaya, Sertac
S Kilickaya
Sertac, Kilickaya
Kilickaya, Sertac
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sertac.kilickaya@ieu.edu.tr
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05.06. Electrical and Electronics Engineering
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| Journal | Count |
|---|---|
| Electrical Engineering | 2 |
| 2025 Symposium on Computational Intelligence on Engineering/Cyber Physical Systems-CIES -- MAR 17-20, 2025 -- Trondheim, NORWAY | 2 |
| 48th Conference of the Industrial Electronics Society-IECON-Annual -- Oct 17-20, 2022 -- Brussels, Belgium | 1 |
| ACM International Conference Proceeding Series | 1 |
| ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering | 1 |
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Master Thesis Microcontroller-Based Real-Time Motor Bearing Fault Detection and Diagnosis Using 1d Convolutional Neural Networks(İzmir Ekonomi Üniversitesi, 2022) Kılıçkaya, Sertaç; İnce, TürkerSürekli makine durum izlemesi, makinelerin durumu ve sağlığı hakkında gerçek zamanlı bilgi sağlaması nedeniyle endüstride beklenmedik makine arızalarını önleyen çok yaygın bir uygulamadır. Dönen makine arızalarının en yaygın nedenlerinden biri rulman arızalarıdır ve rulman arızalarının erken tespiti, motorun kendisinden ziyade arızalı rulmanın değiştirilmesini sağlar. Bu nedenle, elektrik motor rulmanlarının ömrü ve durumu, endüstriyel tesislerin kesintisiz çalışmasını sürdürmek için son kullanıcılar açısından büyük önem taşımaktadır. Geleneksel rulman arıza tespit sistemleri, manuel öznitelikler çıkararak sınıflandırma gerçekleştirir ve yüksek işlem gereksinimi sebebiyle gerçek zamanlı uygulamayı zorlaştırırlar. Öte yandan, 1B Operasyonel Sinir Ağları (1B OSA) ve bunların özel bir durumu olan 1B Evrişimsel Sinir Ağları (1B ESA), otomatik öznitelik çıkarma ve sınıflandırma aşamalarını tek bir öğrenme gövdesinde toplayan daha az işlem gerektiren verimli alternatiflerdir. Bu çalışmada, ilk olarak, 1B OSA'ların ve ESA'ların rulman arıza teşhisindeki etkinliği iki açık kaynak veri seti kullanılarak gösterilmiştir. Ayrıca, İzmir Ekonomi Üniversitesi'ndeki motor test düzeneği kullanılarak iki çeşit tek fazlı asenkron motordan dört farklı rulman sağlığı koşulu için birkaç dakikalık 3 eksen ivmeölçer verisi toplanmıştır. Toplanan veri kullanılarak, bir 1B ESA modeli eğitilip, model katsayıları nicemlendikten sonra Arm Cortex-M4 tabanlı mikrodenetleyiciye yüklenmiştir ve bu sayede gerçek bir motor düzeneğinde modelin rulman arıza teşhis performansı gözlemlenmiştir. Deneysel sonuçlar, 1B ESA'lar kullanılarak düşük güçlü mikrodenetleyiciler ile rulman hatalarının gerçek zamanlı tespit ve teşhisinin mümkün olduğunu göstermektedir.Conference Object Citation - Scopus: 5Smartphone-Based Bearing Fault Diagnosis in Rotating Machinery Using Audio Data and 1d Convolutional Neural Networks(Association for Computing Machinery, 2024) Celebioglu, C.; Kilickaya, S.; Eren, LeventAsynchronous machines are essential components that drive critical systems across industrial, trading, and residential sectors, powering heating units, pumps, and various appliances. Yet, ensuring their reliable process is paramount to prevent costly defects and maintain productivity. Notably, failures in the rolling element bearings (REB) account for about forty percent of motor failures, underscoring the urgency of early detection to mitigate operational risks and financial losses. To address this challenge, this paper proposes an innovative smartphone-based diagnostic technique for detecting bearing faults in induction machines. Leveraging the common availability and computational capabilities of smartphones, the approach utilizes the devices' audio recording functionality to capture motor audio signals. Audio data collected from rotating machines with various fault types is used to train a 1D Convolutional Neural Network (1D CNN), and the trained model is then deployed on a smartphone for real-time fault diagnosis. Embedding this approach into a user-friendly mobile application enhances accessibility and usability, offering a cost-effective solution for fault diagnosis in induction machines. © 2024 ACM.Correction Bearing Fault Detection in Adjustable Speed Drives Via Self-Organized Operational Neural Networks (Oct, 10.1007/S00202-024-02764-3, 2024)(Springer, 2025) Kilickaya, Sertac; Eren, LeventConference Object Citation - Scopus: 3Audio-Based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description(IEEE, 2025) Kilickaya, Sertac; Ahishali, Mete; Celebioglu, Cansu; Sohrab, Fahad; Eren, Levent; Ince, Turker; Gabbouj, MoncefThe frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for effective condition monitoring of machinery. Microphones offer a low-cost alternative to widely used condition monitoring sensors with their high bandwidth and capability to detect subtle anomalies that other sensors might have less sensitivity. In this study, we investigate malfunctioning industrial machines to evaluate and compare anomaly detection performance across different machine types and fault conditions. Log-Mel spectrograms of machinery sound are used as input, and the performance is evaluated using the area under the curve (AUC) score for two different methods: baseline dense autoencoder (AE) and oneclass deep Support Vector Data Description (deep SVDD) with different subspace dimensions. Our results over the MIMII sound dataset demonstrate that the deep SVDD method with a subspace dimension of 2 provides superior anomaly detection performance, achieving average AUC scores of 0.84, 0.80, and 0.69 for 6 dB, 0 dB, and -6 dB signal-to-noise ratios (SNRs), respectively, compared to 0.82, 0.72, and 0.64 for the baseline model. Moreover, deep SVDD requires 7.4 times fewer trainable parameters than the baseline dense AE, emphasizing its advantage in both effectiveness and computational efficiency.Conference Object Citation - Scopus: 6Hyperspectral Image Analysis With Subspace Learning-Based One-Class Classification(Institute of Electrical and Electronics Engineers Inc., 2023) Kılıçkaya, Sertaç; Ahishali, M.; Sohrab, F.; İnce, Türker; Gabbouj, M.Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditional Machine Learning (ML) methods. As the number of frequency bands increases, the required number of training samples increases exponentially to achieve a reasonable classification accuracy, also known as the curse of dimensionality. Therefore, separate band selection or dimensionality reduction techniques are often applied before performing any classification task over HSI data. In this study, we investigate recently proposed subspace learning methods for one-class classification (OCC). These methods map high-dimensional data to a lower-dimensional feature space that is optimized for one-class classification. In this way, there is no separate dimensionality reduction or feature selection procedure needed in the proposed classification framework. Moreover, one-class classifiers have the ability to learn a data description from the category of a single class only. Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data. Overall, this is a pioneer study focusing on subspace learning-based one-class classification for HSI data. We analyze the performance of the proposed subspace learning one-class classifiers in the proposed pipeline. Our experiments validate that the proposed approach helps tackle the curse of dimensionality along with the imbalanced nature of HSI data. © 2023 IEEE.Conference Object Citation - Scopus: 2Thermal 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, MuratCondition 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.Article Padé Approximant Neural Networks for Enhanced Electric Motor Fault Diagnosis Using Vibration and Acoustic Data(Springer Heidelberg, 2025) Kilickaya, Sertac; Eren, LeventPurposeThe primary aim of this study is to enhance fault diagnosis in induction machines by leveraging the Pad & eacute; Approximant Neuron (PAON) model. While accelerometers and microphones are standard in motor condition monitoring, deep learning models with nonlinear neuron architectures offer promising improvements in diagnostic performance. This research investigates whether Pad & eacute; Approximant Neural Networks (Pad & eacute;Nets) can outperform conventional Convolutional Neural Networks (CNNs) and Self-Organized Operational Neural Networks (Self-ONNs) in the diagnosis of electrical and mechanical faults from vibration and acoustic data.MethodsWe evaluate and compare the diagnostic capabilities of three deep learning architectures: one-dimensional CNNs, Self-ONNs, and Pad & eacute;Nets. These models are tested on the University of Ottawa's publicly available constant-speed induction motor datasets, which include both vibration and acoustic sensor data. The Pad & eacute;Net model is designed to introduce enhanced nonlinearity and is compatible with unbounded activation functions such as LeakyReLU.Results and ConclusionPad & eacute;Nets consistently outperformed the baseline models, achieving diagnostic accuracies of 99.96%, 98.26%, 97.61%, and 98.33% for accelerometers 1, 2, 3, and the acoustic sensor, respectively. The enhanced nonlinearity of Pad & eacute;Nets, together with their compatibility with unbounded activation functions, significantly improves fault diagnosis performance in induction motor condition monitoring.Article Citation - WoS: 2Citation - Scopus: 3Bearing Fault Detection in Adjustable Speed Drives Via Self-Organized Operational Neural Networks(Springer Science and Business Media Deutschland GmbH, 2025) Kılıçkaya, Sertaç; Eren, LeventAdjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data. © The Author(s) 2024.Conference Object Pulse Transit Time Based Blood Pressure Estimation in Labview Environment Using Non-Contact Ecg Electrodes and Pulse Sensor(Institute of Electrical and Electronics Engineers Inc., 2019) Guner A.; Kilickaya S.; Bayindir N.S.Sphygmomanometers are conventionally used for measuring blood pressure, namely systolic and diastolic pressures. While measuring the maximum output pressure of the heart, namely systolic, the blood flow is cut with the pressure exerted by the cuff, which may discomfort the patient. To avoid this discomfort, we propose a contactless and cuffless blood pressure measurement system (CBPS) of estimating the blood pressure using non-contact capacitively coupled ECG electrodes and a commercial pulse sensor together with an analog signal acquisition circuitry and a LabVIEW program which calculates the systolic and diastolic pressures from the pulse transit time (PTT). Moreover, non-contact ECG electrodes do not need conductive gel, and the CBPS provides almost instant BP results. These features of the CBPS enable continuous recording of the blood pressure together with the ECG signals in a holter device utilizing contactless electrodes. © 2019 Chamber of Turkish Electrical Engineers.Conference Object Citation - WoS: 3Citation - Scopus: 18Comparison of Different Machine Learning Techniques for the Cuffless Estimation of Blood Pressure Using Ppg Signals(IEEE, 2020) Kilickaya, Sertac; Guner, Aytug; Dal, BarisBlood pressure (BP) is currently measured using sphygmomanometers, and it is a crucial biomarker of a person's heart health. Hence, regular monitoring of blood pressure is important for early diagnosis and treatment. On the other hand, conventional blood pressure measurement devices discomfort patients, since the blood flow is cut off with the pressure exerted by the cuff while measuring systolic blood pressure. Nowadays, researchers are using different signals such as Electrocardiogram (ECG) and Photoplethysmography (PPG) to extract useful information like pulse arrival time (PAT) and pulse transit time (PTT) in order to estimate blood pressure without using a cuff. Two signals can be used simultaneously, but this method requires two sensors, which makes it expensive and unpractical. To overcome this, only PPG-based cuffless and continuous monitoring of blood pressure has been proposed in several studies. In this paper, in order to estimate systolic and diastolic blood pressure values, three different machine learning algorithms, i.e. Linear Regression (LR), Support Vector Regression (SVR) and Artificial Neural Networks (ANNs), were implemented using PPG signals and some other features such as body mass index (BMI), age, height and weight obtained from the patient. A new, short-recorded photoplethysmogram dataset was used for this purpose, and the results are compared in terms of mean absolute error.
