基于声音信号的汽车安全带卷收器质量检测方法研究  

Reserch on Quality Detection Method for Automobile Safety Belt Retractor Based on Sound Signal

作　　者：刘洪达 左敦稳[1] 王勇[1] 靳萌萌 LIU Hongda;ZUO Dunwen;WANG Yong;JIN Mengmeng(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学机电学院,江苏南京210016

出　　处：《机械制造与自动化》2024年第1期271-275,共5页Machine Building & Automation

摘　　要：为提高汽车安全带生产现场质量检测效率,根据《QC/T987—2014汽车安全带卷收器性能要求和试验方法》搭建实验平台,采集卷收器合格品与次品工作过程中的声音信号,将卷积注意力模块(CBAM)嵌入残差网络(ResNet-18)残差块之前,设计CBAM-ResNet-18“Before Blocks”模型,对采集到的卷收器声音信号进行分类。与不加注意力机制的ResNet-18模型、在残差块后加注意力机制的CBAM-ResNet-18“Within Blocks”模型、传统分类模型支持向量机和随机森林相比,模型在卷收器声音信号分类任务中的混淆矩阵、准确率、精确率、召回率和F 1值等方面均表现良好。实验结果表明:所设计的模型对于基于声音信号的汽车安全带卷收器质量检测十分有效。In order to improve the quality inspection efficiency of the automobile seat belt production site,an experimental platform is built according to the“QC/T987—2014 Automotive Seat Belt Retractor Performance Requirements and Test Methods”to collect the sound signals during the working process of the retractor qualified and defective products.Before the convolutional attention module(CBAM)is embedded into the residual network(ResNet-18)residual block,a CBAM-ResNet-18“Before Blocks”model is designed to classify the collected retractor sound signals.Compared with the ResNet-18 model without the attention mechanism,the CBAM-ResNet-18“Within Blocks”model with the attention mechanism after the residual block,the traditional classification model support vector machine and random forest,the designed model performs well in the aspects of confusion matrix,accuracy,precision,recall rate and F 1 value in the task of retractor sound signal classification,which is very effective for the qualitaty detection of automobile safety belt retractors based on the sound signal.

关 键 词：汽车安全带 声音信号 卷收器 质量检测 CBAM-ResNet 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]