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Intelligent Engine Health Monitoring System for Enhanced Vehicle Performance

Dubey Yogita, Damahe Khushal, Ambule Usha, Alone Sanskruti, Deshpande Tanaya, Umate Roshan

Journal of Machine Engineering

2025

Vol. 25, No. 2

89--99

In traditional vehicle maintenance, there’s often no real-time data available, leaving drivers in the dark about important health and safety parameters. This gap can cause problems like low oil levels, poor oil quality, and overheating, which can put the vehicle and passengers at risk. This paper presents the intelligent engine health monitoring system for enhanced vehicle performance. The system uses ESP8266, ultrasonic sensor, light dependent resistor (LDR), and DS18B20 temperature sensors for continuous monitoring of the oil level, oil quality assessment, and engine temperature measurement in real-time. Oil quality assessment using RGB and white light transmission through a glass tube is proposed with improved accuracy in degradation detection. Blynk app interface in the proposed system produces the instant alert for exceeding threshold limit of sensor to ensures enhanced vehicle performance. Results demonstrate that blue light detects early-stage oil degradation, green light provides a balanced evaluation, and red light identifies severe degradation. A comparative analysis with optical color sensors and ultrasonic-based oil detection highlights the system's higher adaptability and real-time monitoring capabilities.

An Artificial Intelligence Based Autonomous Road Lane Detection and Navigation System for Vehicles

Dubey Yogita, Tarte Yashraj, Talatule Nikhil, Sable Bhargav, Taywade Chetan, Umate Roshan

Journal of Machine Engineering

2024

Vol. 24, No. 4

93--107

Lane detection is a foundational technology for autonomous driving systems. It involves identifying the boundaries of lanes on the road and ensuring the vehicle stays within these boundaries. Accurate lane detection is crucial for safety, navigation and traffic management. This paper presents an artificial intelligent based autonomous road lane detection and navigation system for vehicles. The system can detect and analyse road lanes accurately and efficiently in real-time, with frame rates of up to 17 FPS. By utilizing image processing techniques, the system can identify the location and boundaries of road lanes and obstacles on the road and provide accurate and reliable navigation guidance to the driver. The system is integrated with sensors and actuators to provide comprehensive autonomous navigation. The efficiency of the proposed system is demonstrated using accuracy of lane detection on straight and curve lanes covering frames per second and motor of the speed as parameters for assessment. Results show that the proposed system detects lane as entire lane detection, partial lane detection and no detection status on different settings.