一种物料输送管线的堵塞自愈控制方法
By acquiring multiple time-series observation signals in the material conveying pipeline system, and using a dual-channel autoregressive state-space model and an attention-based bidirectional LSTM network for signal-to-noise separation and blockage risk prediction, automatic detection and self-healing control of the material conveying pipeline are realized. This solves the problems of blockage detection delay and high maintenance costs in traditional methods and improves the level of production automation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUAZHONG AGRI UNIV
- Filing Date
- 2025-09-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing material conveying pipeline systems suffer from limited monitoring methods and outdated intervention mechanisms, resulting in delayed blockage detection, high maintenance costs, and environmental risks. They are particularly effective for viscous materials and complex pipeline structures.
By acquiring multiple time-series observation signals, signal-to-noise separation is performed using a dual-channel autoregressive state-space model. Congestion risk is predicted by combining an attention-based bidirectional LSTM network. Signal-to-noise separation is performed based on multiple physical state data from multidimensional observation data. Abnormal signal sequences are extracted, and hierarchical self-healing control is executed based on a self-healing strategy library.
It enables automatic detection and self-healing control of blockages in material conveying pipelines, improving the level of production automation, reducing maintenance time and costs, and lowering environmental risks.
Smart Images
Figure CN121209357B_ABST