基于CEEMDAN-云模型特征熵和LSSVM的磨机负荷预测研究
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摘要
针对球磨机磨矿过程中负荷难以检测和不能准确判断负荷状态的问题,提出了一种基于CEEMDAN-云模型特征熵和LSSVM的磨机负荷预测方法,用完整集成经验分解算法(CEEMDAN)对不同负荷的磨机振动信号进行分解,由相关系数法选取敏感模态分量重构信号,利用逆向云发生器计算重构信号的云模型特征熵作为信号的特征参数,运用正向云发生器生成云模型特征向量的云滴图,结果表明,欠负荷、正常负荷、过负荷之间的熵值差异很大,可以较好地区分和识别磨机负荷状态;将云模型特征向量作为最小二乘支持向量机(LSSVM)的输入,料球比、充填率为输出,建立磨机负荷预测模型;通过磨矿实验验证了该方法的有效性,模型能够准确预测磨机负荷状态。
Aiming at problems of ball mill's load being difficult to detect and load state being not able to be determined in process of ball mill grinding ore, a prediction model for mill load based on IEDA-cloud model feature entropy and LSSVM was proposed. The integrated empirical decomposition algorithm(IEDA) was used to decompose vibration signals of a ball mill under different loads. The sensitive modal components were chosen using the correlation coefficient method to reconstruct a signal. The inverse cloud generator was used to calculate the cloud model's feature entropy of the reconstructed signal as its feature parameter. The forward cloud generator was used to generate the cloud drop diagram of the cloud model feature vector. The results showed that differences among entropy values of under-load state, normal-load one and over-load one are large, and they can be used to better distinguish and identify ball mill's load states. The feature vector of the cloud model was taken as the input of a least squares support vector machine(LSSVM), ratio of material to ball and filling rate were taken as the output to establish a prediction model for ball mill's load, the effectiveness of the proposed model was verified by tests of grinding ore, it was shown that the proposed model can be used to correctly predict load state of ball mills.
Aiming at problems of ball mill's load being difficult to detect and load state being not able to be determined in process of ball mill grinding ore, a prediction model for mill load based on IEDA-cloud model feature entropy and LSSVM was proposed. The integrated empirical decomposition algorithm(IEDA) was used to decompose vibration signals of a ball mill under different loads. The sensitive modal components were chosen using the correlation coefficient method to reconstruct a signal. The inverse cloud generator was used to calculate the cloud model's feature entropy of the reconstructed signal as its feature parameter. The forward cloud generator was used to generate the cloud drop diagram of the cloud model feature vector. The results showed that differences among entropy values of under-load state, normal-load one and over-load one are large, and they can be used to better distinguish and identify ball mill's load states. The feature vector of the cloud model was taken as the input of a least squares support vector machine(LSSVM), ratio of material to ball and filling rate were taken as the output to establish a prediction model for ball mill's load, the effectiveness of the proposed model was verified by tests of grinding ore, it was shown that the proposed model can be used to correctly predict load state of ball mills.
引文
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[13] 罗小燕,陈慧明,卢小江.基于网络搜索与交叉验证的SVM磨机负荷预测.[J] 中国测试,2017,43(1):131-135.LUO Xiaoyan,CHEN Huiming,LU Xiaojiang.Load forecasting of SVM mills based on network search and cross validation[J].China Measurement & Test,2017,43(1):131-135.
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[15] 宋媛,彭利鸿,赖冠文.基于小波包和样本熵的水泵机组振动特征提取[J].中国农村水利水电,2017,3(1):146-152.SONG Yuan,PENG Lihong,LAI Guanwen.Vibration feature extraction of water pump set based on wavelet packet and sample entropy[J].China Rural Water and Hydropowe,2017,3(1):146-152.
[16] 张曹,陈珺,刘飞.基于EMD近似熵和TWSVM的齿轮箱故障诊断[J].煤矿机械,2017,38(4):142-145.ZHANG Cao,CHEN Jun,LIU Fei.Gearbox fault diagnosis based on EMD approximate entropy and TWSVM[J].Coal Mine Machinery,2017,38(4):142-145.
[17] 李余兴,李亚安,陈晓.基于EEMD的舰船辐射噪声特征提取方法研究[J].振动与冲击,2017,36(5):114-119.LI Yuxing,LI Yaan,CHEN Xiao.Research on feature extraction method of ship radiated noise based on EEMD[J].Journal of Vibration and Shock,2017,36(5):114-119.
[18] 杨仁树,付晓强,杨国梁.基于CEEMD与TQWT组合方法的爆破振动信号精细化特征选取[J].振动与冲击,2017,36(3):38-45.YANG Renshu,FU Xiaoqiang,YANG Guoliang.Selection of fine features of blasting vibration signals based on CEEMD and TQWT combination method[J].Journal of Vibration and Shock,2017,36(3):38-45.
[2] SHA Y,CHANG T,CHANG J.Measure methods of ball mill’s load[J].Modern Electric Power,2006,24(35):624-632.
[3] ZHOU P,CHAI T Y,WANG H.Intelligent optimal-setting control for grinding circuits of mineral processing process[J].IEEE Transactions on Automation Science & Engineering,2009,6(4):730-743.
[4] BHAUMIK A,BANERJEE S,SIL J.Designing of intelligent expert control system using petri net for grinding mill operation[J] .Wseas Transactions on Applicatons,2005,4(2):147-150.
[5] 马伦,康建设,孟研.基于Morlet小波变换的滚动轴承早期故障特征提取研究[J].仪器仪表学报,2013,34(4):920-926.MA Lun,KANG Jianshe,MENG Yan.Research on early fault feature extraction of rolling bearing based on Morlet wavelet transform[J].Chinese Journal of Scientific Instrument,2013,34(4):920-926.
[6] LIU Zhuo,CHAI Tianyou,YU Wen.Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation[J].Neurocomputing,2015,169(25):48-60.
[7] TANG J,CHAI T,YU W,et al.Modeling load parameters of ball mill in grinding process based on selective ensemble multisensor information[J].IEEE Transactions on Automation Science & Engineering,2013,10(3):726-740.
[8] TANG J,WANG D,CHAI T.Predicting mill load using partial least squares and extreme learning machines[J].Soft Computing,2012,16(9):1585-1594.
[9] CAO Hui,LI Xinjia,GANG Quansi.An interpolation-based fuzzy controller for load control of ball mill pulverizing system[J].Advanced Materials Research,2011,1231(219):82-94.
[10] ZENG Deliang,WANG Yaohan,GAO Shan,et al.Modeling and control of ball mill system considering coal moisture[J].Measurement,2016,90(36):23-31.
[11] BAI R,CHAI T.Optimization control of ball mill load in blending process with data fusion and case-based reasoning[J].Journal of the Chemical Industry & Engineering Society of China,2009,60(7):1746-1752.
[12] TANG J,ZHAO L,YU W,et al.Soft sensor modeling of ball mill load via principal component analysis and support vector machines[J].Lecture Notes in Electrical Engineering,2010,67(67):803-810.
[13] 罗小燕,陈慧明,卢小江.基于网络搜索与交叉验证的SVM磨机负荷预测.[J] 中国测试,2017,43(1):131-135.LUO Xiaoyan,CHEN Huiming,LU Xiaojiang.Load forecasting of SVM mills based on network search and cross validation[J].China Measurement & Test,2017,43(1):131-135.
[14] YAN X,CHOWDHURY N A.A comparison between SVM and LSSVM in mid-term electricity market clearing price forecasting[J].Electrical and Computer Engineering.IEEE,2013,96(18):1-4.
[15] 宋媛,彭利鸿,赖冠文.基于小波包和样本熵的水泵机组振动特征提取[J].中国农村水利水电,2017,3(1):146-152.SONG Yuan,PENG Lihong,LAI Guanwen.Vibration feature extraction of water pump set based on wavelet packet and sample entropy[J].China Rural Water and Hydropowe,2017,3(1):146-152.
[16] 张曹,陈珺,刘飞.基于EMD近似熵和TWSVM的齿轮箱故障诊断[J].煤矿机械,2017,38(4):142-145.ZHANG Cao,CHEN Jun,LIU Fei.Gearbox fault diagnosis based on EMD approximate entropy and TWSVM[J].Coal Mine Machinery,2017,38(4):142-145.
[17] 李余兴,李亚安,陈晓.基于EEMD的舰船辐射噪声特征提取方法研究[J].振动与冲击,2017,36(5):114-119.LI Yuxing,LI Yaan,CHEN Xiao.Research on feature extraction method of ship radiated noise based on EEMD[J].Journal of Vibration and Shock,2017,36(5):114-119.
[18] 杨仁树,付晓强,杨国梁.基于CEEMD与TQWT组合方法的爆破振动信号精细化特征选取[J].振动与冲击,2017,36(3):38-45.YANG Renshu,FU Xiaoqiang,YANG Guoliang.Selection of fine features of blasting vibration signals based on CEEMD and TQWT combination method[J].Journal of Vibration and Shock,2017,36(3):38-45.