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CHN Energy Released China's first Large-scale Industrial Equipment Diagnostic and Operational Maintenance Model

Author:    Source: Communication Company   Time: 2024-04-05   Font:【L M S

On March 26, the Digital Intelligence Technology Company of CHN Energy through independent research and development, officially launched the country's first comprehensive diagnostic and operational maintenance AI model for industrial equipment, with the model management application platform being put into operation simultaneously. Addressing the current issues in the energy industry such as the diversity of equipment types, complex structures and mechanisms, and the difficulty of operational maintenance, the Intelligent Technology Company has innovatively created the first domestic model capable of comprehensively covering specialized and general equipment in industries such as coal, chemical, and electricity, based on the artificial intelligence foundation built by CHN Energy. This model boasts powerful data processing and text understanding capabilities, standing out in terms of sample coverage, generalization ability, and diagnostic accuracy compared to the general diagnostic models currently used in the industry.

Through the construction of a comprehensive intelligent knowledge base and the development of the "Training Intelligence Assistant" function, the model provides users with convenient and efficient comprehensive equipment operational maintenance guidance services. The management application platform, built on the model, offers operational maintenance personnel a three-dimensional visualization tool that integrates fault location display, disassembly and assembly, and training, helping enterprises achieve intelligent equipment maintenance management, effectively improving operational efficiency, and reducing maintenance costs. The model innovatively adopts a "cloud training, edge application, continuous update" mode. In the cloud, precise training and optimization of the model are carried out based on massive data computation and analysis; at the edge, the model can quickly adapt to different equipment and working scenarios, providing enterprises with real-time, accurate operational maintenance decision support. Through continuous data accumulation and model iteration, the equipment coverage is expanded, and the accuracy and generalizability of equipment monitoring and diagnostics are enhanced.

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