化工学报2019，Vol.70Issue(2)：764-771,8.DOI:10.11949/j.issn.0438⁃1157.20180743
基于MEEMD-多尺度分形盒维数和ELM的球磨机负荷识别方法
Load identification method of ball mill based on MEEMD-multi-scale fractal box dimension and ELM
摘要
Abstract
In view of the problem that the load (filling rate and ball ratio) of a ball mill is difficult to be judged by experience during the grinding process, a method of mill load identification based on the multi-scale fractal box dimension of modified ensemble empirical mode decomposition (MEEMD) and extreme learning machine (ELM) is proposed. Firstly, the MEEMD algorithm is used to decompose the grind signals in different load states to get intrinsic mode components. Then, the correlation coefficient method is used to reconstruct the sensitive modal components to get the signal after noise reduction. By analyzing the multi-scale fractal box dimension of the reconstructed signal. The results show that there are obvious differences in the multi-scale fractal box dimensions of the under load, normal load and overloading state, and it can be well divided into different load states of the mill. The multi-scale fractal box dimension of regrinding signal is used as the input of extreme learning machine (ELM), and the load state of mill is output. The load identification model of mill is established. The effectiveness of the method is verified by grinding experiments. The recognition rate is as high as 94.8%, and the model can accurately identify the mill load status.关键词
MEEMD/分形盒维数/磨机负荷/多尺度/ELMKey words
MEEMD/ fractal box dimension/ mill load/ multi-scale/ ELM分类
信息技术与安全科学引用本文复制引用
蔡改贫,宗路,刘鑫,罗小燕..基于MEEMD-多尺度分形盒维数和ELM的球磨机负荷识别方法[J].化工学报,2019,70(2):764-771,8.基金项目
国家自然科学基金项目(51464017) （51464017）
江西省教育厅科技重点项目(GJJ150618) （GJJ150618）
