一种适用于小箱梁模数式伸缩缝的声纹监测传感器优化布置方法

By optimizing the acoustic sensor placement method and combining the noise reduction effects and dynamic threshold settings of different brands of sensors, the problems of random sensor placement and noise interference were solved, enabling accurate diagnosis of bridge expansion joint defects and improving data quality and diagnostic efficiency.

CN121230867BActive Publication Date: 2026-07-17JSTI GRP CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JSTI GRP CO LTD
Filing Date
2025-09-15
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

The placement of acoustic fingerprint sensors at bridge expansion joints in existing technologies lacks scientific basis, resulting in poor recognition and stability of the collected sound signals, significant background noise interference, and difficulty in achieving accurate and intelligent diagnosis of expansion joint defects.

Method used

By optimizing the placement of the voiceprint monitoring sensors and considering the noise reduction effects and sound pickup differences of different brands of sensors, a sliding window dynamic percentile threshold method is used to set the background noise threshold. The effective waveform energy ratio and signal-to-interference-plus-noise ratio are calculated to ensure that the sound collected by the sensors is clear and stable, and to eliminate the influence of parameter differences among multiple brands of equipment.

Benefits of technology

It enables precise and intelligent diagnosis of bridge expansion joint defects, improves data consistency and reliability, reduces background noise interference, and optimizes the economy and accuracy of sensor deployment.

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Abstract

本发明公开了一种适用于小箱梁模数式伸缩缝的声纹监测传感器优化布置方法。本发明所述声纹监测传感器优化布置方法包括:初步拟定声纹监测传感器桥上和桥下的预设位置;通过声纹监测传感器采集声音,且每一段声音包括背景噪声和车辆撞击伸缩缝声音,对拾取到的背景噪声阈值进行设定;根据设定的背景噪声阈值及音频波形散点计算音频的有效波形能量比和信干噪比;对比有效波形能量比和信干噪比,优化布置传感器。通过本发明方法优化布置声纹传感器,确保采集到的车辆撞击声信号清晰、稳定且受干扰最小,同时消除多品牌设备参数差异对监测结果的影响,提升数据一致性和可靠性,从而实现对伸缩缝病害的精确化、智能化诊断。
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