# Engine Fault Detection by Sound Analysis and Machine Learning
###### Abstract
[18] aimed to identify damages and diagnose damaged components in automotive gearboxes by comparing vibration signals of damaged and undamaged systems. They employed various signal analysis techniques (e.g., wavelet transform and mathematic morphology) to verify damage presence and used a signal processing technique combining pattern spectrum and selective filtering for component failure identification. [PERSON] et al. [19] explored vibration analysis for fault detection in an internal combustion engine and focused on detecting faults related to pop-pet valve clearance and incomplete combustion. They utilized four accelerometers on the engine body, applied the Principal Component Analysis (PCA) technique for data analysis, and achieved high efficiency in fault classification and detection. These studies collectively highlight the wide applicability and effectiveness of vibration-based fault diagnosis in diverse automotive systems. A summary of studies employing vibration signals is given in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Methodology** & **Key Findings** \\ \hline [PERSON] and [PERSON] [17] & 550 signals recorded from a brake system in a controlled environment, 8 brake fault types & Statistical feature extraction and decision trees & Overall accuracy of 97.5\% \\ \hline [PERSON] et al. [18] & Multi-step recording of signals from 13 gearboxes in a lab environment, 3 gearbox fault types & Wavelet transform-based feature extraction and comparison-based classification & Significant differences between signals recorded from gearboxes with and without damage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studies on vehicle fault detection using vibration signals. Although high success rates have been reported in vibration signals, it is hard to implement them in a real-word system. Therefore, a practical method is required to distinguish the faults. Sound signals offer distinct advantages in fault diagnosis due to their ease of recording using commonly available devices such as cell phones or microphones. Therefore, this makes sound analysis a practical and cost-effective approach to diagnosing vehicle faults. Studies conducted by various researchers further exemplify the potential of sound-based fault diagnosis in automotive systems. For instance, [PERSON] et al. [23] identified distinct sounds associated with specific engine malfunctions and developed an algorithm using sound techniques in diagnosis. They reported high error detection rates through analysis of engine sound samples collected from a laboratory environment. Similarly, [PERSON] and [PERSON] [24] developed a fault diagnosis system for internal combustion engines based on the discrete wavelet transform technique applied to sound emission signals and showcased its effectiveness in fault recognition under diverse engine operating conditions. Another study that delved into the application of acoustic signal processing methods for assessing internal combustion engine technical conditions proposed new algorithms for automatic detection of valve clearance issues based on acoustic signal components [25]. Additionally, [PERSON] et al. [26] conducted a study aimed at detecting faults in spark-ignition engines using acoustic signals and an Artificial Neural Network (ANN) system. It highlighted the high potential of ANN-based fault detection in internal combustion engines using acoustic signals, particularly in identifying simulated spark plug and misfire faults. These studies collectively underscore the practicality and efficacy of sound-based fault diagnosis methods in the automotive industry and offer valuable insights for developing reliable diagnostic systems. A summary of such studies employing sound signals is provided in Table 2.  
Despite recent advancements, current studies in vehicle fault diagnosis often encounter significant drawbacks. These limitations include the reliance on data recorded in controlled laboratory settings, which may not fully represent real-world vehicle conditions.