磨煤机振声信号分析及基于BP网的料位识别  

Analysis of Acoustic Signal and BP Neural Network-Based Recognition of Level of Coal in Ball Mill

作　　者：沙毅[1] 曹英禹[1] 郭玉刚 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2006年第12期1319-1323,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金跨科学部交叉重点项目(10433020)

摘　　要：对磨煤机振声信号进行了频谱和功率谱分析,分析表明:低频分量携带磨煤机的料位信息,而高频分量则是由高速电机旋转噪声、排风机噪声以及磨煤机筒体混响噪声引起的;低频料位信号与高频噪声信号是调制关系.利用希尔伯特变换对振声信号进行了解析化处理,分解出低频料位信息,并以振声解析信号的包络为对象,进行料位特征的提取.利用BP神经元网络,建立了磨煤机料位与振声信号的关系模型,从而实现磨煤机料位的自动识别.将模型的计算结果与实测值进行比较,结果表明,料位识别精度在±1.5%之内.Analyzes the frequency and power spectra of acoustic signal due to the vibration of coal that is pulverizing in a ball mill.The results show that the low-frequency signal contains the information on coal level in ball mill,while the high-frequency signal is caused by the noises of working high-speed motor,exhaust fan and rotating drum of ball mill.Both are in a modulating relation.The acoustic signal is analyzed by Hilbert transform,from which the low-frequency signal is picked out to show the corresponding...

关 键 词：磨煤机 振声信号 料位识别 希尔伯特变换 BP神经元网络 

分 类 号：TM621[电气工程—电力系统及自动化]