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A method to identify wet ball mill’s load based on CEEMDAN, RCMDE and SRNN classification
Minerals Engineering ( IF 4.9 ) Pub Date : 2021-03-03 , DOI: 10.1016/j.mineng.2021.106852
Lirong Yang , Jiacheng Cai

Ball mill plays a key role in mineral processing plant, and its load identification for optimal control has great significance for the energy consumption reduction and production efficiency improvement. The vibration signal of ball mill shell contains abundant load information, which can be used to identify ball mill load. However, due to the non-linear and non-stationary characteristics of vibration signals, as well as the heavy background noises, the load identification becomes a challenging task in practice. In this paper, a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), and stacked recurrent neural network (SRNN) is proposed. First, CEEMDAN algorithm is used to decompose the ball mill’s vibration signals and obtain the intrinsic mode function (IMF) components. Then, the sensitive IMF components are selected through the correlation coefficient method, and the signal is reconstructed with the sensitive IMF components. Secondly, the RCMDE of the reconstructed signal is calculated to obtain the load feature vector, and the dimension of the feature vector is reduced by principle component analysis (PCA). Thirdly, the SRNN is applied to establish a load recognition model, taking the feature vector as its input and the load state as its output. The experimental results show encouraging accuracy to apply this approach to recognize the wet ball mill’s load, with a recognition rate of 98.67%.



中文翻译:

基于CEEMDAN,RCMDE和SRNN分类的湿式球磨机负荷识别方法

球磨机在选矿厂中发挥着关键作用,其负荷识别以实现最佳控制对于降低能耗和提高生产效率具有重要意义。球磨机壳体的振动信号包含丰富的载荷信息,可用于识别球磨机载荷。然而,由于振动信号的非线性和非平稳特性以及沉重的背景噪声,在实际应用中,负载识别成为一项具有挑战性的任务。本文提出了一种基于自适应噪声的完全集成经验模态分解(CEEMDAN),精细复合多尺度色散熵(RCMDE)和堆叠递归神经网络(SRNN)的新方法。第一的,CEEMDAN算法用于分解球磨机的振动信号并获得固有模式函数(IMF)分量。然后,通过相关系数方法选择敏感的IMF分量,并使用敏感的IMF分量重建信号。其次,计算重构信号的RCMDE以获得负载特征向量,并通过主成分分析(PCA)来减小特征向量的维数。第三,以特征向量为输入,以负载状态为输出,应用SRNN建立负荷识别模型。实验结果表明,采用这种方法识别湿式球磨机负荷的准确性令人鼓舞,识别率为98.67%。通过相关系数法选择敏感的IMF分量,并使用敏感的IMF分量重建信号。其次,计算重构信号的RCMDE以获得负载特征向量,并通过主成分分析(PCA)来减小特征向量的维数。第三,以特征向量为输入,以负载状态为输出,应用SRNN建立负荷识别模型。实验结果表明,采用这种方法识别湿式球磨机负荷的准确性令人鼓舞,识别率为98.67%。通过相关系数法选择敏感的IMF分量,并使用敏感的IMF分量重建信号。其次,计算重构信号的RCMDE以获得负载特征向量,并通过主成分分析(PCA)来减小特征向量的维数。第三,以特征向量为输入,以负载状态为输出,应用SRNN建立负荷识别模型。实验结果表明,采用这种方法识别湿式球磨机负荷的准确性令人鼓舞,识别率为98.67%。通过主成分分析(PCA)可以减少特征向量的维数。第三,以特征向量为输入,以负载状态为输出,应用SRNN建立负荷识别模型。实验结果表明,采用这种方法识别湿式球磨机负荷的准确性令人鼓舞,识别率为98.67%。通过主成分分析(PCA)可以减少特征向量的维数。第三,以特征向量为输入,以负载状态为输出,应用SRNN建立负荷识别模型。实验结果表明,采用这种方法识别湿式球磨机负荷的准确性令人鼓舞,识别率为98.67%。

更新日期:2021-03-04
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