| 陈子正,行鸿彦,王 瑞,段儒杰.一种铁路隧道衬砌掉块声音检测方法[J].电子测量与仪器学报,2022,36(1):134-140 |
| 一种铁路隧道衬砌掉块声音检测方法 |
| Sound detection method for lining falling block in railway tunnels |
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| DOI： |
| 中文关键词: 铁路隧道 衬砌掉块 声音检测 MFCC 遗传算法 支持向量机 |
| 英文关键词:railway tunnels lining falling block sound detection MFCC genetic algorithm support vector machine |
| 基金项目:国家重点研发计划政府间国际科技创新合作专项(2021YFE0105500)、国家自然科学基金（62171228）、江苏省研究生科研与实践创新计划（SJCX21_0349）项目资助 |
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| Author | Institution |
| Chen Zizheng | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information
Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology |
| Xing Hongyan | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information
Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology |
| Wang Rui | 3. Institute of Electronic
Computing Technology, China Academy of Railway Sciences Corporation Limited |
| Duan Rujie | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information
Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology |
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| 中文摘要: |
| 针对传统铁路隧道衬砌掉块检测方法耗时长、成本高的问题,在声信号识别技术的基础上,提出了基于遗传算法优化支
持向量机(GA-SVM)模型的铁路隧道衬砌掉块声音检测方法。 通过提取铁路隧道内衬砌掉块与其他事件声音的梅尔频率倒谱
系数(MFCC)特征系数矩阵,利用遗传算法的寻优能力对支持向量机中影响预测模型精度的两个参数 C 和 σ 进行优化,构建铁
路隧道衬砌掉块检测模型。 实验结果表明,在少量训练样本的基础上,GA-SVM 模型对比传统的 SVM 模型和粒子群算法
(PSO)优化的 SVM 模型,能更够准确地检测出衬砌掉块的大小,检测精度达到了 96. 67%,验证了声信号识别技术应用于铁路
隧道衬砌掉块检测的可行性。 |
| 英文摘要: |
| In order to solve the problem of long time and high cost of traditional detection methods for falling block in railway tunnel
lining. Based on the acoustic signal recognition technology, this paper proposes a sound detection method for lining falling block in
railway tunnels based on GA-SVM. After extracting the MFCC characteristic coefficient matrix of the lining falling block and other event
sounds in the railway tunnel, this method uses the optimization ability of genetic algorithm to optimize the two parameters C and σ that
affect the accuracy of the prediction model in support vector machine, so as to construct the railway tunnel lining dropped block detection
model. The test results show that compared with the traditional SVM model and the PSO-SVM model, the GA-SVM model can more
accurately detect the size of fallen lining blocks on the basis of a small number of training samples, and the detection accuracy reaches
96. 67%, which verifies the feasibility of the application of acoustic signal recognition technology in the detection of lining falling block of
railway tunnels |
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