Engine Fault Detection by Sound Analysis and Machine Learning

Author:

Akbalık Ferit1,Yıldız Abdulnasır2ORCID,Ertuğrul Ömer Faruk3ORCID,Zan Hasan4ORCID

Affiliation:

1. Social Sciences Vocational School, Batman University, 72040 Batman, Turkey

2. Department of Electrical and Electronics Engineering, Dicle University, 21280 Diyarbakir, Turkey

3. Department of Electrical and Electronics Engineering, Batman University, 72040 Batman, Turkey

4. Vocational School, Mardin Artuklu University, 47200 Mardin, Turkey

Abstract

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.

Publisher

MDPI AG

Reference41 articles.

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