摘要
在列车行驶过程中,车内异响可作为反映车辆设备状态的信息源。为此提出一种基于1D-CNN的识别模型,对车辆异响进行识别,并设计列车异响识别系统。首先构建音频数据的试验样本库,然后利用MFCC提取异响数据样本的特征信息。针对列车噪声特征与车辆状态类型间的映射关系复杂、难解耦的问题,构建一种基于MFCC输入的1D-MCNN对异响所蕴含的故障信息进行识别分类。最后对识别模型进行实验与优化,确定MFCC阶数、学习率与批尺寸等模型参数,采用t-SNE算法、混淆矩阵进行模型特征提取的分析评价。试验结果表明该方法对列车异响识别诊断效果较好,准确率达98.38%。
The abnormal sound in the trains running can be used as information source to reflect the status of the vehicle equipment.For the reason that,a recognition model based on 1D-CNN was proposed to identify the abnormal sound of trains,and a set of recognition system for abnormal sound of trains was designed.Firstly,the experimental sample library of audio data was constructed.Then MFCC was used to extract the characteristic information of abnormal sound data samples.Aiming at the complex mapping relationship between train noise features and vehicle state types,a 1D-MCNN based on MFCC input was constructed to identify and classify the fault information contained in abnormal sound.Finally,the model parameters such as MFCC order,learning rate and batch size are determined by experiments and optimization.The t-SNE algorithm and confusion matrix were used to analyze the model feature extraction ability.The results show that the method is effective for the identification and diagnosis of abnormal sound of trains and its accuracy rate reaches 98.38%.
作者
付孟新
郭世伟
王泽兴
丁建明
Fu Mengxin;Guo Shiwei;Wang Zexing;Ding Jianming(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;State Key Laboratory of Traction Power,Southwest Jiaotong University,Chengdu 610031,China)
出处
《电子测量技术》
北大核心
2023年第14期9-17,共9页
Electronic Measurement Technology
基金
四川科技厅重点研发项目(2020YFG0124)
成都科技局重点研发项目(2019-YF05-01823-SN)
中国博士后科学基金(2020M682506)项目资助
作者简介
付孟新,硕士研究生,主要研究方向为信号处理、故障诊断。E-mail:fmengxin@foxmmail.com;通信作者:郭世伟,副教授,硕士生导师,主要研究方向为测控技术、机械故障诊断、人工智能。E-mail:guoswa11@163.com
