Open Access

ARTICLE

Intelligent Sound-Based Early Fault Detection System for Vehicles

Fawad Nasim^1,2,*, Sohail Masood^1,2, Arfan Jaffar^1,2, Usman Ahmad¹, Muhammad Rashid³

1 Superior University, Lahore, Pakistan
2 Intelligent Data Visual Computing Research (IDVCR), Lahore, Pakistan
3 National University of Technology, Islamabad, Pakistan

* Corresponding Author: Fawad Nasim. Email:

Computer Systems Science and Engineering 2023, 46(3), 3175-3190. https://doi.org/10.32604/csse.2023.034550

Received 20 July 2022; Accepted 28 December 2022; Issue published 03 April 2023

Abstract

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features.

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Keywords

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