Autonomous Vehicles and AI-Based Mechanical Control Systems(GRK)
Autonomous Vehicles and
AI-Based Mechanical Control Systems
Abstract
This seminar paper
presents an in-depth discussion on Autonomous Vehicles and AI-Based Mechanical
Control Systems. The role of Artificial Intelligence (AI) and Machine Learning
(ML) in monitoring, analysing, and improving mechanical systems is explored.
Key principles, architecture, algorithms, applications, advantages, challenges,
case studies, and future directions are highlighted in this work.
Introduction
Condition monitoring
and control of mechanical systems play a vital role in increasing system
reliability, safety, and availability. Traditional mechanical engineering
methods are now enhanced by data-driven AI models to detect faults
automatically and improve performance.
Technology Background
Mechanical components
such as bearings, gears, shafts, engines, and turbines experience continuous
stress and wear during operation. Sensors such as accelerometers,
thermocouples, current sensors, and vibration pickups are used to collect performance
data.
Working Principle
The collected data is
analysed using machine learning techniques to identify abnormal behaviour,
predict failure probability, and assist engineers in making maintenance
decisions.
AI / ML Algorithms Used
Algorithms commonly used
include Artificial Neural Networks, Support Vector Machines, Random Forest,
Decision Trees, Deep Learning, and Time-Series Modelling approaches.
Applications in Mechanical Industry
Applications include
fault detection in rotating machinery, tool wear estimation, structural health
monitoring, vehicle condition monitoring, and real-time control of autonomous
systems.
Case Studies
Industrial case studies
show significant reduction in downtime and maintenance costs when AI-based
monitoring is implemented.
Advantages
Higher machine
availability, predictive alerts, improved safety, reduced cost, and improved
product quality.
Limitations
High initial setup
cost, requirement for skilled manpower, and cybersecurity risks are key
challenges.
Future Scope
Integration of AI with
IoT, Digital Twins, Cloud Computing, and Edge AI will further revolutionize
mechanical engineering industries.
Conclusion
AI and ML are
transforming the way mechanical systems are maintained, monitored, and
controlled. They help engineers make smarter data-driven decisions, improving
efficiency and reliability.
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