基于大数据技术的电气自动化故障诊断系统设计

Key Points

• The study aims to develop a fault diagnosis system for electrical automation using big data technology.
• Four-layer architecture: data collection layer, storage layer, analysis layer, application layer
• Incorporation of machine learning algorithms and an expert knowledge base
• Real-time monitoring of equipment status
• Intelligent extraction of fault features
• Dynamic optimization of diagnosis model
• Achieved fault diagnosis accuracy of 98.7%
• Reduced false alarm rate to 1.2%
• Demonstrated effectiveness in enhancing industrial production safety and efficiency