Physics Model-Based Design for Predictive Maintenance in Autonomous Vehicles Using AI
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In this paper, a model of predictive auto-maintenance is developed. This model is then used for auto-maintenance in the electrical and electronic systems of an autonomous vehicle. Along with traditional sensors, an advanced X-ray vision system is used to detect faults in the system. It uses massively scaled integration (MSI) or very large-scale integration (VLSI) integrated circuitry and other designs to enable the creation of systems containing many components. Commercial applications are being researched and developed, especially in robotics.
In this paper, reconfigurable devices are employed to create fault-tolerant digital systems for the predictive auto-maintenance subsystem of the autonomous vehicle and achieve the highest level of auto-maintenance.
The development of an intelligent, predictive auto-maintenance model is a challenging task that requires the use of modern technologies, ideas, and concepts. Despite recent progress in predictive maintenance, this work takes inspiration and begins with physics models. It seeks to examine the type of deterioration seen in electronic components and connections. Relationships between local failures and global system malfunctions are examined. The aim is to eliminate recurrent system malfunctions through the application of focused auto-maintenance.
The development involves the design and modeling of AI system behavior and takes into account the principles underlying human reasoning on the behavior of technical systems. UML diagrams that visualize human reasoning during occasional self-reflective discussions of the forward reasoning process are presented
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