# Engine Fault Detection by Sound Analysis and Machine Learning
###### 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. vehicle fault detection; extreme learning machines; mel-frequency cepstral coefficients; wavelet transform +
Footnote †: journal: Applied  
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there has been a surge of interest in machine learning-based fault diagnosis systems [5; 6; 7]. Leveraging advancements in artificial intelligence and data analytics, these systems aim to automate fault detection processes, enhance accuracy, and enable proactive maintenance strategies [8; 9]. The general structure of machine learning-based fault diagnosis methods encompasses several stages, i.e., data acquisition, preprocessing, feature extraction, and classification [10; 11]. Data acquisition involves gathering relevant information from vehicles, such as sensor readings and diagnostic codes, and recording relevant signals [12]. Preprocessing focuses on cleaning and organizing the data to eliminate noise and inconsistencies [13]. Feature extraction aims to identify key parameters or characteristics indicative of potential faults [14]. Finally, classification algorithms are applied to categorize the data into different fault types or states [15]. Two primary types of signals, which are vibration and sound signals, are employed in fault diagnosis [16]. Vibration signals capture mechanical movements and dynamics within the vehicle and provide insights into structural integrity and component performance. On the other hand, sound signals reflect acoustic emissions associated with engine operation and component interactions and offer valuable clues about the health and functionality of various vehicle parts. Several studies have explored fault diagnosis using vibration signals and demonstrate the versatility and effectiveness of this approach. For example, [PERSON] and [PERSON] [17] presented a method of vibration-based continuous monitoring system and analysis using a machine learning approach. Their study focused on fault diagnosis in hydraulic braking systems by acquiring vibration signals from a piezoelectric transducer under both good and faulty brake conditions. They employed decision tree algorithms to identify the most relevant features among different faulty conditions and they achieved a classification accuracy of 97.45%. Similarly, [PERSON] et al.