Get in Touch
 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.

Number of participants


Price per participant (excl. VAT)

Testimonials (1)

Upcoming Courses

Related Categories