AI-Powered Vehicle Diagnostics

Abstract

AI-Powered Vehicle Diagnostics improve the accuracy of fault detection by creating a deep learning network that can identify mechanical and electrical faults inside cars. There are several applications of vehicle diagnostics, such as car health monitoring and predictive maintenance, vehicle fault root cause analysis, and federated V2X electric vehicle diagnosis. It is clear that fewer false alarms give mechanics more effective and flexible working hours and improve the field of predictive maintenance because it gives the device that is being predicted at least one more second to either avoid the malfunction by self-healing or at least land in SOS mode. Diagnostics are a mandatory part of automotive repair. Many defects can be visually confirmed or identified using standard test equipment such as a multimeter or a scan tool, or vision recognition software. Modern transport, terrestrial and aerospace (especially for passenger safety reasons), has become a complex interconnection of mechanical, electrical, and tactical components. It is a natural evolution that such a complex system be diagnosed at reduced vehicle downtime and cost. Diagnostics in vehicles have moved on from traditional or empirical methods, such as human-based diagnostics, to rule-based reasoning and fault-based diagnosis. Artificial intelligence-based diagnostics have been used to increase diagnostic accuracy and standalone performance. In the presented work, rather than considering artificial intelligence as a known and incorporated fact, it is assumed that artificial intelligence and interoperable decision-making are the fundamental aspects for developing next-generation automobiles.