Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1A 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.
- 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 SJRQ2A Comparative Study of Time–Frequency Representations for Bearing and Rotating Fault Diagnosis Using Vision Transformer
Orhan, Ahmet; Yordanov, Nikolay; Ertarğın, Merve; Zhilevski, Marin; Mikhov, Mikho
This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to convert the signals into 2D images. A pretrained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting higher sensitivity to bearing-related anomalies.
AtıfScopus29İncele - 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 - MakaleAçık Erişim2023Motor Yataklarında Meydana Gelen Arızaları Tespit Etmek için Yeni Bir Tek Boyutlu Konvolüsyonel Sinir Ağı Modeli
Yıldırım, Özal; Orhan, Ahmet; Ertarğın, Merve
Elektrik motorları, çeşitli işlemleri otomatikleştirme ve kolaylaştırma yeteneklerinden dolayı endüstride önemli bir yere sahiptir. Elektrik motorlarında meydana gelen arızalar, cihazın veya sistemin çalışmasını etkileyebilmekte ve büyük maddi kayıplara neden olabilmektedir. Bu nedenle arızaların erken aşamada tespit edilmesi kritik bir öneme sahiptir. Arızaların tespitinde bilgisayar destekli yazılımlar kullanılması maliyetten ve zamandan tasarruf etme potansiyeli nedeniyle ön plana çıkmaktadır. Bu çalışmada, motor yatağı arıza türlerini tespit etmek için derin öğrenme tabanlı bir model önerilmiştir. Tek boyutlu konvolüsyonel sinir ağı (1D-CNN) mimarisi kullanan bu model ile sadece titreşim verileri kullanılarak arıza tipi tespiti sağlanmaktadır. Önerilen mimari, titreşim sinyallerini motor arıza teşhisinde hızlı ve güvenilir olarak kullanan etkin bir modeldir. Çalışma kapsamında farklı hız senaryoları kullanılarak eğitim ve test aşamalarının detaylı performans değerlendirmeleri sağlanmıştır. Genelleme kabiliyeti yüksek olan bu model ile, farklı senaryolarda yüksek doğruluk oranları ile arıza tespiti yapılmıştır.
AtıfOpenAlex3TR Dizin2
İncele - BildiriAçık Erişim2020Speed Control of DC Motor Using Single Phase Matrix Converter
Boydak, Merve; Orhan, Ahmet; Caliskan, Abuzer
This paper focuses on speed control of DC motor using new driver circuit of Single-Phase Matrix Converter (SPMC). Changing of modulation index has been provided with closed loop PI control. Thus, the voltage applied to the armature has been controlled and rotating of motor at the desired speed has been enabled. Insulated Gate Bipolar Transistors (IGBTs) are used to perform SPMC topology. Sinusoidal Pulse Width Modulation (SPWM) technique is used to obtain switching signals. MATLAB / Simulink is used to implement simulation.
AtıfScopus3WoS3OpenAlex3
İncele - BildiriAçık Erişim2020Using Buck-Boost Rectifier with Single-Phase-Matrix Converter to Reduce Switch Count Operation
Boydak, Merve; Orhan, Ahmet; Caliskan, Abuzer
Recently, the development of advanced power semiconductor devices has made widespread use of power switching circuits and other power electronics applications in commercial and industrial environments. This paper focuses on Single Phase Matrix Converter (SPMC) as a AC-DC boost rectifier and AC-DC buck rectifier. The SPMC was operated in the desired mode using a single driver circuit. Insulated Gate Bipolar Transistors (IGBTs) are used in the power circuit as switching devices to implement SPMC topology. Switching signals are obtained by Sinusoidal Pulse Width Modulation (SPWM) technique. The simulation is carried out using MATLAB / Simulink.
AtıfScopus7WoS3OpenAlex5İncele - BildiriAçık Erişim2020Speed Control of DC Motor under Load Condition Using Single Phase Matrix Converter
Boydak, Merve; Orhan, Ahmet; Caliskan, Abuzer
In this study, speed control of a separately excited DC motor was performed using Single Phase Matrix Converter (SPMC). The driver circuit that operates SPMC in buck-boost mode was used. The DC motor was first run without any controller and the change in speed was observed in the case of loading. Then, the motor was run by using PI controller and speed of the motor was kept constant during loading. Switches in the single-phase matrix converter are switched using Sinusoidal Pulse Width Modulation (SPWM) technique. MATLAB/Simulink was used for simulation.
AtıfScopus1İncele - BildiriYayıncıda açık erişim2020Multimode Operation of Single Phase Matrix Converter with Safe Commutation Strategy
Boydak, Merve; Orhan, Ahmet; Caliskan, Abuzer
In this study, the multimode operation of Single-Phase Matrix Converter (SMPC) is examined. The SPMC was operated as a controlled rectifier and frequency converter using a single driver circuit. Mode selection is provided by changing the value of the constant block in the driver circuit. In the rectifier mode, the sinusoidal input voltage was rectified and transferred to the output. Output voltage at the desired frequency value was generated in the frequency changer mode. Voltage peaks in frequency converter mode were prevented with the safe commutation strategy used. Harmonics in the output voltage were examined for different modulation index values. Switches in the SPMC are switched using Sinusoidal Pulse Width Modulation (SPWM) technique. MATLAB/Simulink was used for simulation.
AtıfOpenAlex1İncele