传感技术学报2015，Vol.28Issue(9)：1361-1366,6.DOI:10.3969/j.issn.1004-1699.2015.09.016
基于CART-LSSVM的球磨机料位软测量方法研究
Soft Sensor for Ball Mill Fill Level Based on CART-LSSVM Model
摘要
Abstract
Ball mill is a high energy consumption equipment used in electricity,grinding and metallurgical indus-tries.Accurate measurement of its fill level(FL)can improve operational efficiency and safety performance. Howev-er,The real-time measurement of FL is difficult to realize,and the components of bearing vibration of ball mill are complex and redundant. Aiming at these problems,a new soft sensor approach of FL based on Classification and Re-gression Tree(CART)and Least Squares Support Vector Machine(LSSVM)is proposed. Firstly,the Power Spectrum density(PSD)of bearing vibration is obtained by welch method,essential features are achieved by partition subse-quently. Secondly,these features are adopted to build CART,and branch nodes of the best model is selected as fea-tures. Finally,the LSSVM are used to implement the non-linear mapping between features and FL. The comparative experiments verifies that this model is feasible and practical with high prediction accuracy.关键词
球磨机料位/软测量/特征选择/分类回归树/最小二乘支持向量机/振动信号Key words
ball mill fill level/soft sensor/feature selection/classification and regression tree/least squares support vector machine/vibration signal分类
信息技术与安全科学引用本文复制引用
张兴,李伟,阎高伟,庞宇松..基于CART-LSSVM的球磨机料位软测量方法研究[J].传感技术学报,2015,28(9):1361-1366,6.基金项目
国家自然科学基金项目(61450011) （61450011）
山西省自然科学基金(2015011052) （2015011052）
