基于声学信号处理的煤矿给煤机智能故障诊断系统设计  

Design of Intelligent Fault Diagnosis System for Coal Mine Feeders Based on Acoustic Signal Processing

作　　者：周涛 冯鹍 温显航 朱朋 宋平 ZHOU Tao;FENG Kun;WEN Xianhang;ZHU Peng;SONG Ping(Dongtan Coal Mine,Yankuang Energy Group Co.,Ltd.,Zoucheng 273500,China)

机构地区：[1]兖矿能源集团股份有限公司东滩煤矿,山东邹城273500

出　　处：《电声技术》2025年第1期35-37,共3页Audio Engineering

摘　　要：针对煤矿给煤机故障诊断面临的挑战,设计一套基于声学信号处理的智能故障诊断系统。该系统采用小波包分解、经验模态分解等方法提取声学信号的敏感特征,并使用Fisher判别准则优选特征子集。在此基础上,构建支持向量机(Support Vector Machine,SVM)诊断模型实现故障的智能识别。通过在某煤矿ZF6000型给煤机上的应用实践,验证了该系统能够快速、准确地诊断输送链磨损、驱动电机轴承故障等典型故障。Aiming at the challenge of fault diagnosis of coal mine feeders,an intelligent fault diagnosis system based on acoustic signal processing is designed.It extracts key features from sounds using methods like wavelet packet decomposition and chooses the best features with the Fisher rule.Then,it uses a machine learning model called Support Vector Machine(SVM)to identify faults.Tests on a ZF6000 feeder showed it can quickly spot common issues like chain wear and motor bearing problems.

关 键 词：煤矿给煤机 故障诊断 声学信号处理 

分 类 号：TH172[机械工程—机械制造及自动化]