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中文摘要：
球磨机是工业领域中广泛应用的大型旋转设备,由于球磨机运行机理和动态响应特性复杂,运行状态参数的预报存在巨大困难.为解决磨机负荷参数预报的问题,对球磨机外部振动信号进行分析,采用核主元分析KPCA的方法对球磨机振动频谱进行特征提取,然后基于特征提取结果建立磨机负荷参数ELM预报模型,实现对球磨机参数负荷磨矿浓度、料球比和填充率的预报.实验结果表明：预报模型具有较高的准确性,更优于传统ELM模型.
英文摘要：
Ball mill is a large rotating equipment widely used in industrial circle.Due to highly complex of the grinding mechanism inside the ball mill and outer dynamic response characteristics,it is difficult to predict running status.In this paper,a method is proposed to solve the problem.It analyses the shell vibration signal of ball mill,extracting vibration frequency spectral features using Kernel Principal Component Analysis（KPCA）.Then prediction models are built by extreme learning machine（ELM） based on the results of KPCA.The result shows the prediction model with a high accuracy and superior to the traditional ELM model.
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