文章基本信息

标题：MULTI-CLASSIFICATION OF BALL MILL LOAD STATUS USING VIBRATION FREQUENCY SPECTRUM
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作者：LIJIE ZHAO ; DECHENG YUAN
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：45
期号：2
页码：694-701
出版社：Journal of Theoretical and Applied
摘要：On-line monitoring and recognition of mill load status has significant effect on the operating efficiency, product quality and energy consumption for the milling circuit. Due to low reliability to recognize the operating states near the boundary region, a multi- classification model is built to identify the operating status of ball mill load. Spectrum features of shell vibration signals are extracted using kernel principal component analysis as input of the multi-classification model. Partial least square-based extreme learning machine model predicts the output coding of ball mill load status. Bayesian decision theory further enhances the reliability and accuracy of the classification model. The proposed method is compared with one-against-one multi-classification strategy and verified with the experimental ball mill. Experimental results show the accuracy and stability of the proposed multi-classification model of ball mill load status outperforms multi-classification with one-against-one strategy.
关键词：Ball Mill; Mill Load State; Multi-class Classification; Vibration Spectrum