基于DCS采集的振动数据的磨机负荷分析
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摘要
检测磨机负荷可了解磨机运转状况以指导生产。以国内某铜矿的半自磨机为研究对象,分析分布式控制系统(DCS)采集的磨机轴承座振动数据和磨机负荷之间的关系。基于主成分分析(PCA)方法从功率和电流数据中提取特征作为负荷参照指标,采用快速傅里叶变换(FFT)方法分析振动信号频谱,通过回归分析与假设检验,验证特定振动传感器的二倍转频频谱与负荷参照指标正线性相关,由此可以用振动传数据的二倍转频频谱表征磨机负荷。
The mill operating conditions could be known by measuring mill load to guide production.This paper takes SAG mill in a domestic copper ore concentrator as the research object,analyzes the relationship between the vibration data of the bearing housings from DCS and the mill load.Extracts features from power and current data as the mill load reference indicator based on PCA,analyzes the vibration signal spectrum by FFT,through regression analysis and hypothesis test,verifies that the double rotation frequency spectrum of the particular vibration sensors are all positively linearly related to the mill load reference.Thus,the double rotation frequency spectrum of the vibration data can be used to characterize the mill load.
The mill operating conditions could be known by measuring mill load to guide production.This paper takes SAG mill in a domestic copper ore concentrator as the research object,analyzes the relationship between the vibration data of the bearing housings from DCS and the mill load.Extracts features from power and current data as the mill load reference indicator based on PCA,analyzes the vibration signal spectrum by FFT,through regression analysis and hypothesis test,verifies that the double rotation frequency spectrum of the particular vibration sensors are all positively linearly related to the mill load reference.Thus,the double rotation frequency spectrum of the vibration data can be used to characterize the mill load.
引文
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[2]周平,柴天佑.磨矿过程磨机负荷的智能监测与控制[J].控制理论与应用,2008,25(6):1095-1098.
[3]张杰,王建民,杨志刚.模糊神经网络在磨机负荷控制中的应用[J].仪表技术与传感器,2014(5):66-68.
[4]杨志刚,赵莉娅,薄敬东.基于磨音信号的磨机负荷模型[J].河北理工大学学报(自然科学版),2011(5):65-69.
[5]YINGMIN YI,HAICHUAN YANG,LU SUN,et al.The ball mill load measuring algorithm though grinding tone signal based on GA[C].Proceedings of 2017International Seminar on Artificial Intelligence,Networking and Information Technology.Bangkok,Thailand,2017:7-13.
[6]杨佳伟,陆博,周俊武.基于振动信号分析的球磨机工况检测技术的研究与应用[J].矿冶,2013,22(3):99-104.
[7]罗小燕,卢小江,熊洋,等.小波分析球磨机轴承振动信号特征提取方法[J].噪声与振动控制,2016,36(1):148-152.
[8]刘志刚,蔡改贫,林龙飞,等.主元分析的振动频域特征识别与磨机负荷建模研究[J].中国钨业,2016,31(3):68-73.
[9]许永强,姜志宏,蔡改贫,等.基于AR模型的磨机振动信号特征提取方法[J].中国钨业,2017,32(2):71-76.
[10]周志华.机器学习[M].北京:清华大学出版社,2016:229.
[11]张洪亭,王明赞.测试技术[M].沈阳:东北大学出版社,2005:182.
[12]陈希孺,倪国熙.数理统计学教程[M].合肥:中国科学技术大学出版社,2009:378.