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Duration 14 hours
Course Outline
Introduction to Edge AI in Industrial Automation
- Examining the scope of Edge AI and its industrial applications.
- Analyzing the advantages and complexities of Edge AI in industrial environments.
- Reviewing case studies of successful Edge AI adoption in manufacturing.
Establishing the Edge AI Environment
- Installation and configuration of essential Edge AI tools.
- Setting up industrial sensors and data acquisition systems.
- Exploring pertinent Edge AI frameworks and libraries.
- Practical exercises focused on environment configuration.
Predictive Maintenance via Edge AI
- Foundations of predictive maintenance strategies.
- Developing AI models for monitoring equipment health.
- Implementing real-time fault detection and forecasting.
- Practical sessions on predictive maintenance implementation.
Quality Control through Edge AI
- Overview of quality control mechanisms in manufacturing.
- AI methodologies for defect identification and categorization.
- Deploying vision-based systems for quality assurance.
- Practical exercises for quality control applications.
Process Optimization with Edge AI
- Introduction to process optimization techniques.
- Leveraging AI for real-time process monitoring and control.
- Implementing AI-driven decision-making frameworks.
- Practical exercises dedicated to process optimization.
Deployment and Management of Edge AI Solutions
- Rolling out AI models onto industrial edge devices.
- Oversight and maintenance of Edge AI infrastructure.
- Troubleshooting and refining deployed models.
- Practical exercises on deployment and management protocols.
Tools and Frameworks for Industrial Edge AI
- Survey of key tools and frameworks (such as TensorFlow Lite, OpenVINO).
- Utilizing TensorFlow Lite for industrial AI tasks.
- Practical exercises involving optimization tools.
Real-World Applications and Case Studies
- Analysis of successful industrial Edge AI initiatives.
- Exploration of sector-specific use cases.
- Capstone project: constructing and refining a real-world industrial AI application.
Conclusion and Future Directions
Requirements
- Foundational knowledge of AI and machine learning principles.
- Professional experience with industrial automation systems.
- Elementary programming proficiency (Python is preferred).
Target Audience
- Industrial engineers.
- Manufacturing professionals.
- AI developers.
Testimonials (1)
That we can cover advance topic and work with real-life example