摘要
针对选煤厂巡检自动化的需求,提出一种基于声音增强技术的智能巡检机器人设计方案。该方案采用麦克风阵列采集环境声音,通过卡尔曼滤波、梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)特征提取、长短期记忆网络(Long Short-Term Memory,LSTM)诊断设备异常。实验结果表明,该机器人在复杂环境下表现出优异的故障诊断与运动控制性能,故障检出率大于93%,定位误差小于5 cm,为建设智能选煤厂提供了可行的解决方案。
A design scheme for an intelligent inspection robot based on sound enhancement technology is proposed to meet the demand for automated inspection in coal preparation plants.This scheme uses a microphone array to collect environmental sound,and diagnoses equipment abnormalities through Kalman filtering,Mel Frequency Cepstrum Coefficient(MFCC)feature extraction,and Long Short-Term Memory(LSTM)network.The experimental results show that the robot exhibits excellent fault diagnosis and motion control performance in complex environments,with a fault detection rate of over 93%and a positioning error of less than 5 cm,providing a feasible solution for the construction of intelligent coal preparation plants.
作者
孙亚妮
张子恩
SUN Yani;ZHANG Zien(Ordos Yingpanhao Coal Industry Co.,Ltd.,Ordos 017300,China)
出处
《电声技术》
2024年第9期164-166,共3页
Audio Engineering
关键词
选煤厂
智能巡检
声音增强
coal preparation plant
intelligent inspection
sound enhancement
作者简介
孙亚妮(1999-),女,本科,助理工程师,研究方向为智能化;通信作者:张子恩(2000—),女,本科,研究方向为智能化。E-mail:2420461500@qq.com。
