Edge AI in IIoT (Industrial Internet of Things)

In today's IoT era, the vast amount of data from interconnected devices requires advanced AI systems to process it. Edge AI involves deploying AI software on local hardware, enhancing automation, productivity, and security across industries. Industry 5.0, evolving from Industry 4.0, focuses on creating resilient, worker-centric, and sustainable manufacturing processes.

Key uses of edge AI in manufacturing include:

• Predictive Maintenance: Sensors detect anomalies early, preventing machine breakdowns and reducing downtime.
• Quality Control: Real-time defect detection improves efficiency and reduces waste.
• Equipment Efficiency: Monitoring equipment in real-time ensures adherence to quality standards.
• Yield Optimization: Sensor data helps machines adjust parameters to improve product quality without manual oversight.

Challenges in AI implementation include data accuracy, justifying models, and transparency. Issues like bias, opacity, and data security also pose risks. Solutions involve transparent, white-box models to enhance trust and accountability

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