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Industrial AI
  • Collection and management of diverse IoT sensor data for industrial safety management
  • Beyond simple monitoring, risk prediction through AI and big data integration
  • Preventive management

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Analysis and Response
Case
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Diagram
Analysis and Response
Case
• Simple monitoring based only on dryer internal or exhaust temperature
• Risk judgment of carbonization (overheating, ignition) relying on worker experience
• Limitations in detecting subtle anomalies using temperature changes alone
• Restricted real-time detection of gas emissions such as CO and CO2
• High accident rate due to failure in early detection
• Quality degradation and safety incidents linked to production
• Real-time detection based on multiple sensors
• Early warning and immediate response upon real-time sensor risk signal detection
• Rapid detection of carbonization to prevent fire and quality incidents
• Minimizing equipment downtime and production loss
• Risk assessment using clear numeric criteria such as temperature variation and CO/CO2 concentration
• Minimizing judgment variability among workers and human errors
• Real-time monitoring of environment, equipment, and worker status
• Automatic detection and alerting of risk factors
• Risk prediction and preventive maintenance based on AI and big data analysis
• Management of confined and high-risk areas through camera integration
• Data integration and visualization
• Automated data storage and management
• Remote inspection and maintenance
Diagram
Analysis and Response
Case
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