摘要
为了实现岩巷掘进机不同工况截割岩壁岩石硬度的识别,通过掘进机多截割工况岩石硬度识别实验平台,获得不同工况截割不同硬度岩石的截割电机电流及扭矩信号,提出一种基于特征优选和随机森林(RF)的掘进机截割岩壁岩石硬度识别方法。该方法首先利用自适应噪声的完全集合经验模态分解(CEEMDAN)对实验获得的电流及扭矩信号进行分解,获得本征模态分量(IMF)并计算IMF的样本熵(SE),将SE最高的IMF进行变分模态(VMD)二次分解。计算二次分解IMF的模糊熵后对电流及扭矩信号重构,再计算重构信号的时频特征,与二次分解IMF的模糊熵组成电流及扭矩信号的时-频-熵特征。为了避免特征过多影响模型识别,提出Relief-F结合Pearson相关系数的特征选择方法,最后通过乌燕鸥算法(STOA)优化随机森林(RF)的最大特征数和决策树个数,完成了不同截割工况下岩石硬度识别模型的建立。结果表明:(1)对电流及扭矩信号先CEEMDAN分解再VMD分解可以降低原始信号的随机性和波动性,相较于CEEMDAN及VMD的一次分解,岩石硬度识别准确度分别提升15.2%和23.9%;(2)不同截割工况下岩石硬度识别,电流信号的时域特征占岩石硬度识别准确度权重最大;(3)提出的Relief-F结合Pearson相关系数的特征优选方法对3种工况下截割4种硬度岩石的电流及扭矩信号特征聚类明显;(4) STOA对RF关键系数的选取有优化效果,且算法迭代次数少,相较于传统的粒子群优化算法,以不同工况截割不同硬度岩石识别准确度提高7.2%。
In order to realize the identification of rock hardness when the roadheader cuts rock wall under different working conditions,a rock hardness identification method based on the feature preference and the STOA-random forest(RF)is proposed.A test bench is built for the rock hardness identification when a roadheader cuts under multiple working conditions,obtaining some motor current and torque signals from cutting different hardness rocks under different working conditions.The method first decomposes the experimentally obtained current and torque signals by using the complete ensemble empirical modal decomposition of adaptive noise(CEEMDAN)to obtain the eigenmodal component(IMF)and calculates the sample entropy(SE)of the IMF,and the IMF with the highest SE is subjected to the quadratic decomposition of the variational modal decomposition(VMD).After calculating the fuzzy entropy of the secondary decomposited IMF,the current and torque signals are reconstructed,and the time-frequency features of the reconstructed signals are calculated,and the time-frequency-entropy features of the current and torque signal samples are composed with the fuzzy entropy.In order to avoid too many features affecting the recognition model,the method of Relief-F combined with Pearson correlation coefficient is proposed to select the features for dimensionality reduction.Finally,the maximum number of features and the number of decision tree in RF are optimized by the STOA method with the highest recognition accuracy as the fitness function,and the establishment of different rock hardness recognition models under different truncation conditions is completed.The results show that①the CEEMDAN decomposition and then the VMD decomposition of current and torque signals can reduce the randomness and volatility of original signals,and the accuracy of rock hardness identification can be improved by 15.2%and 23.9%,respectively,compared with the primary decomposition of CEEMDAN and VMD.②The time domain features of current signals account for the greatest weight of rock hardness identification accuracy under different cutting conditions.③The proposed Relief-F combined with Pearson correlation coefficient feature selection method has obviously clustered the current and torque signal features of four hardness rocks cut under three working conditions.④The STOA has optimized the selection of RF key coefficients,and the number of iterations of the algorithm is less,compared with the traditional particle swarm optimization algorithm,the recognition accuracy of different hardness rocks cut under different working conditions is improved by 7.2%.
作者
常聚才
戚鹏飞
陈潇
CHANG Jucai;QI Pengfei;CHEN Xiao(School of Mining Engineering,Anhui University of Science and Technology,Huainan 232000,China)
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2023年第2期1070-1084,共15页
Journal of China Coal Society
基金
安徽省重点研究与开发计划资助项目(202004a07020045)
安徽省高校协同创新资助项目(GXXT-2019-029)。
