基于KNN算法与φ-OTDR系统的高铁声屏障故障识别方法

Fault Identification Method of High⁃speed Railway Noise Barriers Based on KNN Algorithm andφ⁃OTDR System

提出了一种基于K近邻(K-nearest neighbors,KNN)算法和相位敏感光时域反射(Phase-sensitive optical time domain reflectometry,φ-OTDR)系统的高铁声屏障故障识别方法。设计了V字型光缆敷设方式,能够感知声屏障不同高度吸声板在脉动力冲击下的振动,并利用φ-OTDR系统采集振动信号。对振动信号进行多域特征提取以及K近邻分类后,可以实现对声屏障故障状态识别。实验结果表明,在复杂场景下对于故障点的识别正确率达到了90.9%。该方法为声屏障故障识别提供了一条可行的技术路线,能够减少对专业人员的依赖,对于提升高铁声屏障智能运维水平具有重要意义。

A method of recognizing faults in noise barriers of high-speed railways was proposed based on the K nearest neighbors(KNN)algorithm and the phase-sensitive optical time domain reflectometry(φ‑OTDR)system.A V‑shaped laying method of optic fiber cable was designed to sense vibrations of sound absorption boards at different heights of the noise barrier.And vibration signals under air turbulent force were acquired by theφ‑OTDR system.After the multi‑domain feature extraction and KNN classification of vibration signals,the state of noise barriers could be recognized.Results of the experiment showed that average recognition accuracy of 90.9%could be obtained even under complex field environments.This method could provide a feasible technical route for the fault detection of noise barriers,which could reduce dependence on professionals,so as to play an important role in improving the level of intelligent operation and maintenance.