Sürdürülebilir Kalkınma Amaçları

• 17
• 8
• 2
• 1
• 1

• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1382
  A smartphone-based vibration dataset for induction motor fault diagnosis under different speed and load conditions

  Ertarğın, Merve; Orhan, Ahmet; Yıldırım, Özal; Chauhan, Sumika; Vashishtha, Govind

  Induction motors are foundational components across industrial applications, valued for their inherent robustness, operational simplicity, and cost-efficiency. Maintaining their reliable function is paramount for a wide range of mechanical and electrical systems. However, they are prone to various mechanical and electrical faults, such as bearing defects, rotor issues, and voltage imbalances, which can significantly impair their performance and reliability. This study presents a novel vibration dataset for induction motor fault diagnosis, uniquely acquired using a smartphone-based inertial sensor rather than conventional industrial accelerometers. Vibration signals were recorded along three orthogonal axes (gx, gy, gz), alongside gravity-compensated acceleration components (guserx, gusery, guserz), enabling detailed analysis of both raw and gravity-free vibration characteristics. Data were collected under diverse conditions, including healthy operation and several fault types, across varying rotational speeds and load states. The dataset features long-duration vibration recordings sampled at 100 Hz, suitable for both time-domain analysis and window-based feature extraction. Its inclusion of multiple operating speeds and load conditions is ideal for studying the impact of operational variability on fault signatures. By leveraging low-cost and readily accessible smartphone sensors, this dataset supports practical and accessible vibration data acquisition for supporting the development, benchmarking, and validation of data-driven fault diagnosis methods. This resource is expected to significantly advance research in condition monitoring of induction motor, particularly for machine learning and signal processing applications using vibration data.

  • 
  İncele
• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/811
  Hybrid Supervised Classification and Deep Embedding–Based Profiling Framework for Electricity Consumption Analysis

  Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Zhilevski, Marin; Mikhov, Mikho; Yordanov, Nikolay

  This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management.

  • 
  • 
  • 
  AtıfScopus1·OpenAlex1

  İncele
• BildiriAçık Erişim2025/r/munzur/1953
  Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models

  Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup

  The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models.

  • 
  İncele
• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1379
  Automated fault classification of asynchronous motor using mobile phone accelerometer and Parallel Residual CNN-GRU

  Ertargin, Merve; Orhan, Ahmet; Yildirim, Ozal; Gurgenc, Turan

  In this study, a fault detection system that classifies motor faults using deep learning without requiring any sensor connections inside or on the motor setup is proposed. The focus is on the electrical and mechanical faults of asynchronous motors, which are widely used in the industry. A mobile application developed using Flutter can collect vibration data using the three-axis accelerometer sensor in smartphones. By placing a smartphone with the application installed on the motor for which fault detection is desired, vibration data on three axes (x, y, z) is collected to train deep models. Subsequently, test data is used to identify the fault status and class of the electric motor. Different conditions and scenarios were created to test the adequacy of smartphone accelerometer sensors in detecting motor faults. Fault classification with vibration data obtained under different motor operating speeds in loaded and unloaded conditions achieved accuracy rates of over 99%.

  • 
  AtıfScopus18·OpenAlex15

  İncele
• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/986
  Species classification of microalgae using a CNN-based deep learning approach under optimal cultivation conditions

  Simsek, Gokce Kendirlioglu; Ertargin, Merve; Pezzolesi, Laura; Pistocchi, Rossella; Yildirim, Ozal; Cetin, A. Kadri

  Microalgae possess significant potential in a wide range of applications due to their valuable bioactive compounds. They are utilized in biofuel production to reduce dependence on fossil fuels, in wastewater treatment for the biological removal of heavy metals and pollutants, in carbon capture for mitigating climate change, and in the pharmaceutical and nutraceutical industries as supplements. As their applications expand, the accurate and efficient identification of microalgal species becomes increasingly important. Traditional classification methods are time-consuming and rely heavily on expert knowledge. The main aim of this study is to develop a reliable, fast, and expert-independent deep learning-based approach for the classification of microalgae species using microscopic images. In this context, deep learning techniques, specifically convolutional neural network (CNN)-based models were employed to classify microalgal species. Four widely used pre-trained CNN architectures (ResNet152, DenseNet201, MobileNetV2, and EfficientNetB0), along with a custom-designed CNN, were implemented. The models were trained and tested on a labeled dataset consisting of microscopic images of Chlorella vulgaris, Scenedesmus acutus, and Haematococcus pluvialis. The classification models achieved accuracy rates ranging from 96.87 % (Custom CNN) to 100 % (DenseNet201), demonstrating the potential of CNN-based approaches in automating and improving microalgae species identification.

  AtıfScopus5·WoS5·OpenAlex5

  İncele
• MakaleYayıncıda açık erişim2024Scopus SJRQ2/r/munzur/2237
  Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

  Ertargin, Merve; Yildirim, Ozal; Orhan, Ahmet

  The utilization of monitoring sensors in machinery has led to the mainstream adoption of fault detection and diagnosis in time series data across various industrial applications. Deep learning techniques, specifically in constructing fault diagnosis models by extracting insights from historical equipment fault data, are receiving widespread attention as crucial tools in ensuring the safety and reliability of motor systems. In this study, a CNN-LSTM-based deep learning model is proposed for the detection of electric motor faults. Three distinct sets of accelerometer sensor data are provided as input to the model, enabling a comprehensive evaluation of its performance across various sensor configurations. The model demonstrated a remarkable capacity for generalization, achieving impressive accuracy rates of 99.96% for Accelerometer-1, 98.88% for Accelerometer-2, and 99.37% for Accelerometer-3. This underscores the robustness and adaptability of the proposed CNN-LSTM model in effectively detecting electric motor faults regardless of the specific accelerometer sensor employed.

  AtıfScopus47·OpenAlex38

  İncele
• BildiriAçık Erişim2024/r/munzur/1692
  Classifying induction motor faults using spectrogram images with deep transfer learning

  Ertargin, Merve; Yildirim, Ozal; Orhan, Ahmet

  For industrial systems to be reliable and efficient, motor faults detection is important. Conventional methods of fault detection are typically expensive and time-consuming. The ability to detection motor faults has advanced significantly in recent years thanks to the application of deep learning techniques. Deep learning algorithms have the ability to automatically extract complicated features from big data sets, which can speed up and improve the accuracy of motor faults detection. Since collecting motor acoustic data is cost-effective and easy, it is advantageous to use it in fault detection. In this study, motor acoustic signals were converted into spectrograms and faults in induction motors were detected using a transfer learning approach with pre-trained models. Totally 8-class fault detection was performed with an accuracy rate of 91.52% using VGG16 and 92.11% using VGG19.

  • 
  AtıfScopus4·OpenAlex4

  İncele
• BildiriAçık Erişim2021/r/munzur/2936
  Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction over Q Wave Analysis

  Yildirim, Ozal; Baloglu, Ulas B; Talo, Muhammed; Ganesan, Prasanth; Tung, Jagteshwar S; Kang, Guson; Tooley, James; Alhusseini, Mahmood I.; Baykaner, Tina; Wang, Paul J.

  Clinical screening of myocardial infarction is important for preventative treatment and risk stratification in cardiology practice, however current detection by electrocardiogram Q-wave analysis provides only modest accuracy for assessing prior cardiac events. We set out to evaluate the ability of a deep neural network trained on the electrocardiogram to identify patients with clinical history of myocardial infarction. We assessed 608 patients at two academic centers with adjudicated history of myocardial infarction. Surface electrocardiograms were used to train a neural network-based model that classifies patients with and without a history of infarction. Endpoints were assessed by clinical record review and accuracy of the model was compared against the manual assessment of pathologic Q waves. The neural network outperformed the accuracy of pathologic Q waves (62%). In training, the model accuracy converged to >98%. Validation was performed by cross-validation (k=5) with validation accuracy 71 ± 5%. Receiver-operator characteristics analysis resulted in a c-statistic of 0.730. Deep learning of a 12-lead ECG can identify features of prior myocardial injury more accurately than clinical Q-wave analysis and may serve as a valuable clinical screening tool.

  • 
  AtıfScopus3·OpenAlex3

  İncele
• MakaleYalnız künye2020/r/munzur/6793
  Derin sinir ağları kullanarak türkiye’deki otomobil satışlarının tahmini için bir model

  Kaya, Sema Kayapinar; Yıldırım, Özal

  Tüm kararlarda itici güç olan tedarik zinciri sürecinde talep tahmini, tedarik zinciri sürecinin en önemli bileşenlerinden birisidir. Gelecekteki ürün ve hizmetlerin tahmin edilmesi, diğer tüm tahminlerin başlangıç noktası olup diğer tüm süreçlerin temel girdisini oluşturmaktadır. Bu çalışmada, otomobil satış tahmini için 8 katmanlı Derin Sinir Ağ (DSA) modeli önerilmiştir. Modelin girdileri, döviz kuru, gayrisafi yurt içi hasıla, tüketici güven endeksi ve tüketici fiyat endeksi gibi çeşitli ekonomik göstergelerden oluşmaktadır. Modelin çıkışına göre araç satış tahmini yapılmıştır. 2011 ve 2018 yılları arasında aylık bazda toplam 90 veri toplanılarak analizler yapılmıştır. Elde edilen sonuçlar, bu yaklaşımın çeşitli satış tahmin problemlerinde kullanılabileceğini göstermektedir.

  AtıfTR Dizin5

  İncele
• MakaleYalnız künye2020Scopus SJRQ1/r/munzur/3825
  A deep convolutional neural network model for automated identification of abnormal EEG signals

  Yıldırım, Özal; Baloglu, Ulas Baran; Acharya, U. Rajendra

  Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.

  • 
  AtıfScopus164·OpenAlex211

  İncele