Experimental analysis of wet mill load parameter based on multiple channel mechanical signals under multiple grinding conditions
Experimental analysis of wet mill load parameter based on multiple channel mechanical signals under multiple grinding conditions
复制标题
多种磨削条件下基于多通道机械信号的湿磨负荷参数实验分析
DOI：
10.1016/j.mineng.2020.106609
复制
发表时间：
2020-12
影响因子：
4.8
通讯作者：
Sheng Ning
中科院分区：
文献类型：
--
作者：
Tang Jian;Yan Gaowei;Liu Zhuo;Liu Yefeng;Yu Gang;Sheng Ning
在线监测球磨机内部负荷参数是提高选矿过程生产质量和产量的关键。本文提出了基于多通道机械信号的湿磨机负载参数（MLP）的实验分析。通过一系列实验来研究不同研磨条件下的机械频谱特性，例如纯球、矿物或水负载变化。基于功率谱密度 (PowSD)，详细解释了不同 MLP 的多通道机械信号，即矿物与球体积比 (MBVR)、矿浆密度 (PD) 和电荷体积比 (CVR)。实验结果表明，这些机械信号的PowSD与CVR呈正相关，与MBVR和PD呈负相关。进一步定性分析了这些机械信号的产生机制，提出了一种新的多通道机械信号贡献率的测量方法，即组合估计指标。结果表明，在不同的研磨条件下，这些信号对各种 MLP 的贡献率不同。必须针对不同的MLP选择合适的机械通道来构建有效的MLP预测模型。
Online monitoring load parameters inside the ball mill is the key to improving the production quality and quantity of the mineral grinding process. In this paper, the experimental analysis of wet mill load parameter (MLPs) based on multiple channel mechanical signals is presented. A series of experiments is conducted to investigate the mechanical frequency spectrum characteristics in terms of different grinding conditions, such as only-ball, -mineral, or –water load change. Based on power spectra density (PowSD), multiple channel mechanical signals are interpreted for different MLPs, i.e., mineral-to-ball volume ratio (MBVR), pulp density (PD), and charge volume ratio (CVR), in detail. Experimental results show that the PowSDs of these mechanical signals are positively correlated with CVR and negatively correlated with MBVR and PD.Further, the generation mechanism of these mechanical signals is qualitatively analyzed, and a new measurement method for the contribution rate of multiple channel mechanical signals, i.e., combination estimation index, is proposed. The results show the different contribution rates of these signals to various MLPs under varied grinding conditions. Appropriate mechanical channels for different MLPs must be selected to construct an effective MLP forecasting model.
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影响因子：
4.8
作者：
Sarada Prasad Das;D. Das;S. Behera;B. Mishra
通讯作者：
Sarada Prasad Das;D. Das;S. Behera;B. Mishra
影响因子：
12.3
作者：
J. Faiz;V. Ghorbanian;B. Ebrahimi
通讯作者：
J. Faiz;V. Ghorbanian;B. Ebrahimi
用于区分摄影图像和计算机图形的四元数小波域取证特征分析
影响因子：
3.6
作者：
Wang, Jinwei;Li, Ting;Ye, Jingyu
通讯作者：
Ye, Jingyu
影响因子：
8.4
作者：
Lei, Yaguo;He, Zhengjia;Zi, Yanyang
通讯作者：
Zi, Yanyang
DOI：
10.3724/sp.j.1004.2013.01744
发表时间：
2013
期刊：
Acta Automatica Sinica
影响因子：
--
作者：
C. Tian
通讯作者：
C. Tian