# Engine Fault Detection by Sound Analysis and Machine Learning
###### Abstract
Furthermore, many studies are constrained to specific car models or fault types, limiting the generalizability and applicability of their findings. There is a pressing need for a comprehensive dataset that encompasses diverse vehicle models, real-world vehicle conditions, and a wide range of fault scenarios to enhance the effectiveness and accuracy of fault diagnosis systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Methodology** & **Key Findings** \\ \hline [PERSON] et al. [19] & Signals recorded from a car engine in a controlled environment & Statistical features of PCA components and comparison-based classification & High accuracy in fault classification \\ \hline [PERSON] et al. [20] & 600 signals recorded from a car engine in a lab environment, 7 engine fault types & Time–frequency domain feature extraction and artificial neural networks & Overall accuracy of 97\% \\ \hline [PERSON] and [PERSON] [21] & Signals recorded from a car engine in a lab environment, in cases involving faulty and healthy injectors & Time–frequency analysis & Significant differences between signals recorded from the engine with and without faulty injectors \\ \hline [PERSON] et al. [22] & 411 signals recorded from 8 valve train states in a controlled environment & Time–frequency analysis and probabilistic neural networks & Overall accuracy of 97.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Our study directly addresses these challenges by leveraging a diverse and extensive dataset comprising real-life vehicle sounds. This dataset captures a wide array of vehicle conditions various vehicle models, and an extensive range of fault scenarios encountered in everyday driving. We employ advanced signal processing techniques, including Mel-Frequency Cepstral Coefficients (MFCCs), Wavelet Transform, and Relief-F methods, for robust feature extraction and feature selection, while using Extreme Learning Machines (ELM) for the classification. The summary of our study's approach and key contributions is as follows:  
* Instead of employing a laboratory-collected dataset, comprehensive data were collected from real-life vehicle conditions and diverse vehicle models. * In order to increase the success of the proposed approach, advanced signal processing techniques, which are MFCC, Wavelet Transform, and Relief-F, were employed. * A thorough frequency analysis was conducted in each fault type, and specific frequency components, which are associated with different types of faults, were identified. This paper is structured as follows. Section 2 gives details about utilized data collection methods and focuses on how sound signals were acquired from vehicular systems under various operating conditions. Section 3 elaborates on employed methodology encompassing employed signal processing techniques and feature extraction methodologies that are used in vehicle fault diagnosis based on sound signals. In Section 4, we present the obtained experimental results, which includes performance evaluations of the employed diagnostic system, comparisons with existing methods in the literature, and a detailed frequency