湿式球磨机筒体振动信号分析及负荷软测量  

Analysis of Vibration Signal of Wet Ball Mill Shell and Soft Sensoring for Mill Load

作　　者：汤健[1] 赵立杰[1] 岳恒[1] 柴天佑[1] 

机构地区：[1]东北大学流程工业综合自动化教育部重点实验室,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2010年第11期1521-1524,共4页Journal of Northeastern University(Natural Science)

基　　金：国家科技支撑计划项目(2008BAB31B03);中国博士后自然科学基金资助项目(20100471464)

摘　　要：针对磨矿过程的磨机负荷(ML)难以有效检测,球磨机常运行在欠负荷状态,造成该过程难以实现优化控制和节能降耗的难题,通过综合分析球磨机筒体振动的产生机理、不同研磨条件下振动信号的功率谱密度(PSD)及ML参数与PSD各频段的相关性,提出了采用料球比、浓度及充填率三个负荷参数对ML进行软测量建模的方法.该方法首先将振动加速度的时域信号通过傅立叶变换至频域,然后采用主元分析法(PCA)对振动频谱数据的低、中、高三个频段分别进行降维和特征谱变量的提取,最后利用最小二乘支持向量机(LSSVM)实现特征谱变量与ML参数间的非线性映射.实验结果表明,该算法能够有效地提取频谱变量的谱特征,并具有较高的估计精度.It is difficult to detect the mill load（ML） efficiently in the ore milling process of a ball mill and the mill is often underloading,thus causing the difficulty in implementing the optimal control with energy saving and cost reduction. To solve the problems,how the vibration of mill shell is generated is analyzed,as well as the power spectrum density （PSD） of the vibration signals under different milling conditions and the correlation between the PSD and ML parameters within different frequency bands. Then,the way to model the soft sensoring is proposed introducing the three ML parameters,i.e.,the mineral to ball volume ratio in a mill,pulp density and charge volume ratio. With the vibration acceleration signals of the mill shell in time domain transformed into frequency domain by fast Fourier transform （FFT）,the principal component analysis （PCA） is used for dimensionality reduction and extraction of the principal components （PCs） of characteristic spectra within the low,medium and high frequency bands separately. Finally,the least square support vector machines （LSSVM） are used to implement the mapping between characteristic spectral PCs and ML parameters. Experimental results showed that the way by FFT-PCA-LSSVM can extract the spectral characteristics efficiently with higher prediction accuracy than other conventional methods.

关 键 词：磨机负荷(ML) 功率谱密度(PSD) 主元分析(PCA) 最小二乘支持向量机(LSSVM) 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]