AI-Based Fault Diagnosis of Car Engines Using Multi-Sensor Data Fusion / (Record no. 611725)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01683nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240923134616.0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.382,NAQ |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Akbar Naqvi, Syed Muhammad Ali |
| 9 (RLIN) | 125990 |
| 245 ## - TITLE STATEMENT | |
| Title | AI-Based Fault Diagnosis of Car Engines Using Multi-Sensor Data Fusion / |
| Statement of responsibility, etc. | Syed Muhammad Ali Akbar Naqvi, Alishba Zahid, Muhammad Rehan Munir Janjua, Amna Bibi. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | MCS, NUST |
| Name of publisher, distributor, etc. | Rawalpindi |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 74 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Modern 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | UG EE Project |
| 9 (RLIN) | 118090 |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME | |
| Geographic name | BEE-57 |
| 9 (RLIN) | 125983 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Dr. Shibli Nisar |
| 9 (RLIN) | 112570 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Project Report |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | General Stacks | 09/23/2024 | 621.382,NAQ | MCSPTC-483 | 09/23/2024 | 09/23/2024 | Project Report |