000			01683nam a22001817a 4500
003			NUST
005			20240923134547.0
082			_a621.382,NAQ
100			_aAkbar Naqvi, Syed Muhammad Ali _9125990
245			_aAI-Based Fault Diagnosis of Car Engines Using Multi-Sensor Data Fusion / _cSyed Muhammad Ali Akbar Naqvi, Alishba Zahid, Muhammad Rehan Munir Janjua, Amna Bibi.
260			_aMCS, NUST _bRawalpindi _c2024
300			_a74 p
505			_aModern automobiles rely on sophisticated Engine Control Units (ECUs) to manage various performance aspects. However, in an Internal Combustion engine, a small fault can lead to bigger and multiple problems, resulting in unexpected breakdowns and high repair costs. To address this issue, this paper presents an AI-based fault diagnostic system that integrates multiple sensors to predict and identify engine faults, such as Misfires, Piston knocks, and Starting/Stability Malfunctions. By leveraging neural networks for multi-sensor data fusion, the system enables real-time analysis of sensor data, improving fault prediction accuracy and adaptability to evolving fault patterns. The integration of neural networks with sensor data fusion represents a significant advancement in automotive diagnostics, supporting our commitment to delivering efficient fault diagnostic solutions. This AI-based early detection system aims to minimize repair costs and inconvenience for vehicle owners, highlighting the importance of predictive maintenance in ensuring vehicle reliability and performance.
650			_aUG EE Project _9118090
651			_aBEE-57 _9125983
700			_aSupervisor Dr. Shibli Nisar _9112570
942			_2ddc _cPR
999			_c611721 _d611721