Sürdürülebilir Kalkınma Amaçları
Yayınlar
- BildiriYalnız künye2025Scopus SJRQ4A Hybrid Machine Learning Architecture for Acoustic Fault Diagnosis in Electric Motors: Offline and Online Modes via Mobile Phone
Yordanov, Nikolay; Zhilevski, Marin; Mikhov, Mikho; Ertargin, Merve
This paper proposes a hybrid ML architecture for non-invasive acoustic fault diagnosis in electric motors and machines using a.NET MAUI cross-platform mobile app for short audio capture (5-20s), audio-to-image transformations and dual-mode inference: embedded ML.NET/ONNX models for offline preliminary detection and Azure ML-hosted deep CNN/RNN for online probabilistic multi-fault analysis. The primary objective of this paper is to demonstrate the benefits of the proposed architecture and to show the specific tools which can be used for implementation. Architectures (embedded, cloud, hybrid) are evaluated by listing main advantages and disadvantage, highlighting hybrid approach that combines offline resilience, reduced latency and keeping the possibility for complex analysis by using online hosted model. Sample mobile application design is added to showcase the proposed user experience.
- MakaleAçık Erişim2025Scopus SJRQ1Automated 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%.
AtıfScopus18OpenAlex15
İncele - MakaleAçık Erişim2025Scopus SJRQ2Species 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.
AtıfScopus5WoS5OpenAlex5
İncele - MakaleYayıncıda açık erişim2024Scopus SJRQ2Mechanical 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.
AtıfScopus47OpenAlex38İncele - BildiriAçık Erişim2024Classifying induction motor faults using spectrogram images with deep transfer learning
Ertargin, Merve; Yildirim, Ozal; Orhan, Ahmet; Benedicenti, L.
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.
AtıfScopus4OpenAlex4İncele