Steam pipeline system health state real-time evaluation and early warning method and related equipment
By establishing a three-dimensional geometric model and a fluid-solid-thermal multi-field coupled solution method, combined with deep neural networks and damage rules, real-time health status assessment and early warning of high-parameter steam pipelines were realized. This solved the problems of large computational load, weak generalization ability and lack of foresight in the existing technology, and achieved accurate and adaptive monitoring and optimization of steam pipeline systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN THERMAL POWER RES INST CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for health monitoring and assessment of high-parameter steam pipelines suffer from problems such as large computational load making real-time online assessment difficult, weak data model generalization ability, and lack of forward-looking and quantitative suggestions in early warning, leading to frequent false alarms and missed alarms, making it difficult to achieve rapid and accurate assessment and early warning of stress state across the entire domain.
A three-dimensional geometric model is established and the mesh is refined. A fluid-solid-thermal multi-field coupling solution method is used to simulate multiple operating scenarios, generating a working condition-stress database. Lightweight prediction is performed by combining a deep neural network model. Data is collected in real time through a distributed control system and dual-path parallel computing is performed. Real-time evaluation and early warning are performed by combining damage rules. An optimization suggestion is generated by design decision support algorithm.
It enables real-time, accurate, and adaptive health status assessment and early warning for high-parameter steam pipeline systems, improving the reliability of assessments and the foresight of early warnings, reducing false alarms and missed alarms, providing quantitative optimization suggestions, forming closed-loop management, and reducing maintenance costs.
Smart Images
Figure CN122407988A_ABST