Tunnel fan health diagnosis and early warning system and method based on multi-source fusion

The multi-source fusion tunnel ventilation health diagnosis and early warning system utilizes various sensors and intelligent diagnostic algorithms to solve the problems of delayed fault detection and insufficient early warning in the operation and maintenance management of tunnel ventilation, achieving accurate fault identification and predictive maintenance, and improving operation and maintenance efficiency and equipment stability.

CN121382684APending Publication Date: 2026-01-23NANJING TUNNEL & BRIDGE ADMINISTRATION CO LTD
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Patent Information

Application Number
CN202511871654.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-12
Publication Date
2026-01-23

AI Technical Summary

Technical Problem

The existing operation and maintenance management model for tunnel ventilation fans suffers from high degree of blindness, long inspection cycles, and strong subjectivity. It is difficult to capture sudden failures and hidden dangers, lacks collaborative analysis of multi-source data, and cannot achieve predictive maintenance, resulting in delayed fault detection and insufficient early warning capabilities.

Method used

A tunnel ventilation fan health diagnosis and early warning system based on multi-source fusion is adopted, which includes a perception layer, a data acquisition and transmission layer, a data processing and fusion layer, an intelligent diagnosis and early warning layer, and an application layer. It collects data by deploying multiple sensors, uses deep belief networks and convolutional neural networks for fault diagnosis, generates hierarchical early warning information by combining an adaptive threshold mechanism, and provides intelligent diagnosis functions through a B/S architecture.

Benefits of technology

It enables accurate identification and early warning of fan failures, improves the accuracy of fault diagnosis, reduces the reliance on maintenance personnel, enhances maintenance response efficiency, extends the service life of key components, and ensures the stable operation of the tunnel ventilation system.

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Abstract

The invention relates to the technical field of tunnel monitoring, and discloses a tunnel fan health diagnosis and early warning system and method based on multi-source fusion, comprising a sensing layer, a data acquisition and transmission layer, a data processing and fusion layer, an intelligent diagnosis and early warning layer and an application layer which are arranged in sequence from bottom to top by adopting a layered architecture, all the layers cooperate to achieve tunnel fan collision safety and equipment health two-dimensional management and control. Vibration, displacement, strain, temperature and power multi-source monitoring data are fused, single-parameter monitoring limitation is broken through, wind turbine typical faults such as bearing damage, coupling misalignment and foundation looseness and specific risks such as over-limit vehicle collision are covered, and a full-dimension monitoring system is constructed. By means of a deep belief network (DBN), a convolutional neural network (CNN) and an MATLAB core algorithm, and in combination with a fault-feature mapping library and a multi-evidence combined judgment mechanism, the fault diagnosis accuracy is greatly improved.
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