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Browsing by Author "Akbalik, Ferit"

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    Article
    Engine Fault Detection by Sound Analysis and Machine Learning
    (Mdpi, 2024) Akbalik, Ferit; Yildiz, Abdulnasir; Ertugrul, Omer Faruk; Zan, Hasan; Zan, Hasan
    Traditional vehicle fault diagnosis methods rely heavily on the expertise of mechanics or diagnostic tools available at service centers, which can be costly, time-consuming, and may not always provide accurate results. This study presents a comprehensive vehicle fault diagnosis framework, which utilized Mel-Frequency Cepstral Coefficients (MFCCs), Discrete Wavelet Transform (DWT)-based features, and the Extreme Learning Machine (ELM) classifier. To address the limitations of previous works, the proposed framework leverages a large, diverse dataset encompassing various vehicle models and real-world operating conditions. Significantly improved robustness and generalizability of the fault diagnosis system were achieved. The results of the experiments demonstrate the superiority of the MFCC-based features combined with the ELM classifier, achieving the highest performance metrics in terms of accuracy, precision, recall, F1-score, macro F1-score, and weighted F1-score, which are 92.17%, 92.24%, 92.22%, 92.10%, and 92.06%, respectively. Slightly lower performance was obtained while employing the DWT-based features compared to employing MFCC-based features. Additionally, frequency analysis was conducted to identify specific frequency bins, which are the most indicative of different fault types in providing valuable guidance for future diagnostic efforts. Overall, the proposed framework provides a reliable and practical solution for accurate vehicle fault detection, paving the way for future advancements in automotive diagnostics.
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    Enhancing Vehicle Fault Diagnosis Through Multi-View Sound Analysis: Integrating Scalograms and Spectrograms in a Deep Learning Framework
    (Springer London Ltd, 2025) Akbalik, Ferit; Yildiz, Abdulnasir; Ertugrul, Omer Faruk; Zan, Hasan; Zan, Hasan
    This study presents a comprehensive framework for vehicle fault diagnosis using engine sound signals, leveraging deep learning models and a multi-view approach. Traditional methods for vehicle fault diagnosis often rely on the expertise of mechanics or diagnostic tools, which can be costly, time-consuming, and may not always provide accurate results. To address these limitations, we propose CarFaultNet, a multi-view model that processes both scalograms and spectrograms simultaneously to capture complementary information from these time-frequency representations. Our approach incorporates transfer learning with pretrained convolutional neural networks, including AlexNet, GoogLeNet, ShuffleNet, SqueezeNet, and MobileNet v2, as well as CarFaultNet, which combines two MobileNet networks. The results demonstrate that CarFaultNet outperforms traditional machine learning methods and single-view deep learning models, achieving a precision of 95.32%, recall of 94.83%, F1-score of 94.99%, and accuracy of 95.00%. Class activation mapping visualizations provide valuable insights into the model's decision-making process, highlighting the regions of the input images that are most influential for the classification of different vehicle faults. By leveraging a large, diverse dataset encompassing various vehicle models and real-world operating conditions, our approach addresses the drawbacks of previous studies and demonstrates the potential of deep learning for practical and effective vehicle fault diagnosis.