AUTOMOTIVE DIAGNOSTICS: A TECHNICAL ANALYSIS AND INTEGRATED SYSTEMS APPROACH

Abstract

Automotive diagnostics plays a crucial role in modern vehicle maintenance and repair systems. With the increasing complexity of electronic control units (ECUs), onboard diagnostics (OBD), and sensor networks, traditional methods are no longer sufficient. This paper presents a comprehensive technical analysis of current diagnostic technologies, emphasizing fault detection, system integration, and real-time data acquisition. We investigate both software-based and hardware-based diagnostic tools, as well as the integration of AI and machine learning algorithms for predictive maintenance. The study proposes a unified diagnostic framework combining sensor fusion, cloud-based analytics, and standardized protocols such as OBD-II and UDS. Experimental results demonstrate the effectiveness of the integrated approach in improving accuracy, reducing diagnostic time, and enhancing vehicle safety and reliability.