A Fault Detection Strategy for Wheel Flat Scars with Wavelet Neural Network and Genetic Algorithm

doi:10.7652/xjtuxb201309015

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A Fault Detection Strategy for Wheel Flat Scars with Wavelet Neural Network and Genetic Algorithm

更新时间：2025-07-09

- A Fault Detection Strategy for Wheel Flat Scars with Wavelet Neural Network and Genetic Algorithm
- Vol. 47, Issue 9, Pages: 88-91+111(2013)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201309015
  CLC： O341
- Online First：10 September 2013，
  
  Published：2013
- 稿件说明：
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A Fault Detection Strategy for Wheel Flat Scars with Wavelet Neural Network and Genetic Algorithm[J]. 2013, 47(9): 88-91+111.
DOI：

A Fault Detection Strategy for Wheel Flat Scars with Wavelet Neural Network and Genetic Algorithm[J]. 2013, 47(9): 88-91+111. DOI： 10.7652/xjtuxb201309015.

摘要

为了寻求一种更加有效的列车车轮扁疤故障分析算法

提出一种通过轮轨噪声来确定车轮扁疤严重程度的检测方法。该方法将遗传算法与小波神经网络相结合

同时为了避免出现局部极小值

加速学习速度

在小波神经网络中增加了动量模型; 在搜寻小波神经网络隐含层链接权值之前

使用遗传算法进行计算以优化小波神经网络结构; 硬件只需2组麦克风阵列以及2个速度感应器就可以提供实时结果

成本远低于我国现有的检测方法。对不同列车车速下的轮轨信号进行了实时测试

结果表明: 与传统神经网络、小波神经网络和遗传算法相比

该方法的检测准确率最多分别提高了16%、11%和3%

并且收敛最快。

Abstract

A novel strategy is proposed to provide a more effective wheel flat scar fault detection algorithm by means of wheel/rail noise. In this strategy

genetic algorithm is combined with a wavelet neural network

and a momentum model is added into the genetic wavelet neural network to avoid the local minimum and to accelerate the learning speed. Before searching the hidden-layer weights of the network

the structure of the network is optimized by genetic algorithm. This strategy requires only two groups of microphone arrays and two speed sensors for real-time measurements. Consequently

the cost is much lower than that of the existing detection methods in China. The proposed strategy has been applied to the real-time detection of train wheel/rail signals at different speeds. Numerical results reveal that the proposed strategy is of the fastest convergence

and its detection accuracy increases at most by 16%

10%

and 3%

respectively

compared with the conventional neural network

wavelet neural network

and genetic algorithm.

关键词

Keywords

references

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Related Institution

College of Computer Science and Technology, Huaqiao University

State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University

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