# Engine Fault Detection by Sound Analysis and Machine Learning
## 4 Results and Discussion
### Comparison with Other Studies  
Numerous publications have addressed the diagnosis of vehicle malfunctions through engine sound analysis, employing a diverse array of methods. For instance, Navea and Sybingco [27] used Fourier transform and power spectral density to detect engine starting issues, drive belt problems, and valve-related faults, achieving a detection accuracy of 56% using recordings from the 1996-2000 Honda Civic model. [PERSON] et al. [28] focused on misfire faults in four vehicles, using Fourier transform, wavelet transform, and MFCC with SVM, resulting in a 99% accuracy rate. [PERSON] et al. [5] recorded audio from a Santana 2000's engine cylinder head, using Hilbert-Huang transform and SVMs, achieving up to 90% accuracy. [PERSON] and [PERSON] [41] studied lubrication, chain, crank, and valve faults in Honda Unicorn and Bajaj Pulsar motorcycles, employing MFCC and achieving accuracy rates between 50% and 75%. The reported results in the literature are summarized in Table 8.  
A significant drawback of previous works is their reliance on data from controlled laboratory settings, often using the same brand of vehicles or a limited number of fault types. These controlled conditions do not adequately capture the variability and complexity of real-world scenarios, leading to models that may not perform well outside the specific conditions under which they were trained. Furthermore, the homogeneity of vehicle models in these studies limits the generalizability of their findings, as the diagnostic methods may not be applicable to different vehicle makes and models.  
This dataset includes various types of faults and different environmental settings, thereby enhancing the robustness and generalizability of our fault diagnosis framework. By utilizing data from multiple vehicle brands and real-world conditions, the employed approach is designed to reflect the true complexity and variability of vehicle faults.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Methodology** & **Key Findings** \\ \hline \multirow{3}{*}{[PERSON] et al. [5]} & 140 signal recordings from & Hilbert–Huang transform for & \\  & Santana 2000 model vehicle & feature extraction and support & \\  & Santana 2000 model vehicle & vector machines for & \\  & & classification & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [27]} & 36 signals recorded from the & Fourier transform and power & \\  & 1996–2000 model Honda Civic, & spectrum density for feature & \\  & 3 engine-related faults & extraction and fuzzy logic & \\  & & inference for classification & \\ \hline \multirow{3}{*}{[PERSON] et al. [28]} & 992 2.5-long signals recorded & Fourier, wavelet transform, & \\  & from 4 cars, one type of & and MFCC-based feature & \\  & engine fault & extraction and support & \\  & & vector machines & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [41]} & Sound samples of motorcycles & \\  & under idling conditions are & & \\ \cline{1-1}  & recorded using a voice & & \\ \cline{1-1}  & recorder with 44.1-kHz & & \\ \cline{1-1}  & sampling frequency and 16-bit & & \\ \cline{1-1}  & quantization. & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Reported results in the literature.