Intelligent railway fastener based on delta F judgment and burr suppression

By combining ΔF judgment and burr suppression with EMA and MA filtering algorithms and burr identification, the railway intelligent fastener solves the monitoring misjudgment problem during the installation and operation phases, and achieves high-precision and stable tension signal processing.

CN120907714APending Publication Date: 2025-11-07TAIYUAN PENGYUE ELECTRONIC TECH CO LTD +1
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Patent Information

Application Number
CN202511199341.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-26
Publication Date
2025-11-07

AI Technical Summary

Technical Problem

Existing monitoring technologies for intelligent railway fasteners struggle to accommodate high-order algorithms with high computational demands during installation and operation, resulting in a high false alarm rate and susceptibility to environmental noise and mechanical vibration.

Method used

It employs a state recognition module and an adaptive filtering module based on ΔF determination, combining the exponentially weighted moving average (EMA) filtering algorithm and the moving average (MA) filtering algorithm, and introduces a glitch recognition module to freeze abnormal signals, supporting remote control of filtering mode switching by the wireless communication module.

Benefits of technology

This improves the monitoring accuracy and stability of intelligent railway fasteners under different working conditions, reduces the risk of misjudgment, and enhances the system's adaptability and ease of operation and maintenance.

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Abstract

The invention relates to the field of railway vehicle fastener tension monitoring, in particular to a railway intelligent fastener based on delta F judgment and burr suppression, and aims to improve the monitoring precision of the railway intelligent fastener. Comprising a tension sensor and a central processing unit, the central processing unit comprises a state recognition module and a self-adaptive filtering module, and the state recognition module is connected with the tension sensor and the self-adaptive filtering module. The state recognition module collects a tension signal output by the tension sensor at a fixed period as a sampling value, calculates a difference value between the sampling value at the current moment and the sampling value at the previous moment, and sends the tension signal to the tension sensor when the difference value is larger than a first set threshold value and smaller than or equal to the first set threshold value. And respectively judging whether the railway intelligent fastener is in an installation state or an operation state. And the adaptive filtering module receives a state judgment result, and performs smoothing processing on the tension signal by adopting an exponential weighted moving average filtering algorithm and a moving average filtering algorithm respectively in an installation state and an operation state.
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