数字孪生驱动的多模态无源传感列车转向架监测方法及系统
The digital twin-driven multimodal passive sensing train bogie monitoring system utilizes onboard excitation and receiving units to construct an electromagnetic energy field. A distributed sensor network senses the electromagnetic energy and operates. Combined with FFT analysis and multi-parameter fusion evaluation, it solves the problems of complex wiring, high maintenance, signal collision, and low diagnostic accuracy in train bogie monitoring, achieving efficient monitoring and fault diagnosis throughout the entire life cycle.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING JIAOTONG UNIV
- Filing Date
- 2026-03-20
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for monitoring train bogies suffer from problems such as complex wiring, high maintenance costs of active wireless monitoring, passive sensors that can only monitor a single static physical quantity, easy collision of signals from multiple sensors, and low accuracy of fault diagnosis. Furthermore, they lack digital twin technology integration, making it impossible to achieve real-time mapping between physical sensor data and virtual models.
The multimodal passive sensing train bogie monitoring system driven by digital twins constructs an electromagnetic energy field by transmitting a single-frequency continuous wave radio frequency signal through the on-board excitation and receiving unit. The distributed passive multimodal sensing network senses the electromagnetic energy and operates. The integrated signal processing and diagnostic unit performs FFT analysis to decouple the signal components and achieve multi-parameter fusion evaluation.
It achieves passive wireless maintenance-free monitoring, breaking the limitation of traditional passive sensors that only measure a single static quantity, avoiding signal collisions, improving the accuracy of fault diagnosis, and meeting the monitoring needs of the entire train life cycle.
Smart Images
Figure CN121877421B_ABST