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基于包络谱分析的车轮圆度误差检测

Wheel Out-of-Roundness Detection Using an Envelope Spectrum Analysis.

机构信息

CONSTRUCT-LESE, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.

出版信息

Sensors (Basel). 2023 Feb 14;23(4):2138. doi: 10.3390/s23042138.

Abstract

This paper aims to detect railway vehicle wheel flats and polygonized wheels using an envelope spectrum analysis. First, a brief explanation of railway vehicle wheel problems is presented, focusing particularly on wheel flats and polygonal wheels. Then, three types of wheel flat profiles and three periodic out-of-roundness (OOR) harmonic order ranges for the polygonal wheels are evaluated in the simulations, along with analyses implemented using only healthy wheels for comparison. Moreover, the simulation implements track irregularity profiles modelled based on the US Federal Railroad Administration (FRA). From the numerical calculations, the dynamic responses of several strain gauges (SGs) and accelerometer sensors located on the rail between sleepers are evaluated. Regarding defective wheels, only the right wheel of the first wheelset is considered as a defective wheel, but the detection methodology works for various damaged wheels located in any position. The results from the application of the methodology show that the envelope spectrum analysis successfully distinguishes a healthy wheel from a defective one.

摘要

本文旨在使用包络谱分析检测铁路车辆的车轮踏面和多边形化车轮。首先,简要介绍了铁路车辆车轮的问题,特别关注车轮踏面和多边形化车轮。然后,在模拟中评估了三种车轮踏面轮廓和三种多边形车轮的周期性失圆(OOR)谐波阶数范围,并与仅使用健康车轮进行的分析进行了比较。此外,模拟实现了基于美国联邦铁路管理局(FRA)的轨道不平整度轮廓建模。从数值计算中,评估了位于轨枕之间的轨道上的几个应变计(SG)和加速度计传感器的动态响应。对于有缺陷的车轮,仅考虑第一个轮对的右侧车轮为有缺陷的车轮,但检测方法适用于任何位置的各种损坏的车轮。该方法的应用结果表明,包络谱分析能够成功区分健康车轮和有缺陷的车轮。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e3/9966101/8294cfa2caa7/sensors-23-02138-g001.jpg

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