基于智能磨矿介质及CNN和优化SVM模型的球磨机负荷识别方法

Ball mill load status identification method based on the convolutional neural network,optimized support vector machine model,and intelligent grinding media

当前球磨机负荷检测方法难以准确评估磨机内部变化,给磨机综合运行状态的控制和优化带来较大难度.本文设计了一款内嵌加速度传感器且与钢球介质物理性质相一致的智能磨矿介质用于识别磨机负荷,开展了不同充填率等磨矿条件下的磨矿试验,设计磨矿效果系数划分磨机负荷状态;分别采用了卷积神经网络方法(CNN)和优化的支持向量机(SVM)模型对智能磨矿介质获取的加速度信号进行球磨机负荷识别.基于优化的SVM模型将获取的一维加速度信号进行互补集合经验模态分解算法(CEEMD)去噪、时域特征值和样本熵提取等预处理,将上述磨机负荷的特征向量分别输入GA-SVM、GS-SVM、PSO-SVM分类模型进行训练,研究表明,PSO-SVM模型的识别准确率可达98.33%,但存在训练过程繁琐,耗费时间长的问题.在图像识别领域具有优秀应用能力的CNN模型是把智能磨矿介质检测加速度信号数据转换为二维图片后直接输入基于VGG19网络的CNN模型进行分类识别,磨机负荷分类识别准确率高于优化的SVM模型,可达98.89%,在保证识别准确率的同时有效节约了计算时间.基于CNN的智能磨矿介质球磨机负荷识别方法可为实现球磨机负荷检测与在线评估提供重要解决方案与技术保障.

A ball mill is important grinding equipment in a concentrator,and the accurate detection of the load status ensures that the ball mill runs in the best state,which helps optimize the grinding process,ensure the stable operation of the ball mill equipment,and save energy.The current mainstream detection methods cannot easily detect the movement inside the ball mill.Mill load requires a more efficient and direct detection method.In this study,the SM φ500 mm×500 mm ball mill was taken as the research object.Through theoretical analysis and simulation,intelligent grinding media with an embedded triaxial acceleration sensor and physical properties similar to that of ordinary steel ball media were designed to identify the mill load,and grinding experiments with different filling rates and other grinding conditions were conducted.Results revealed that the filling rate and the material to ball ratio are the important factors affecting the-0.074 mm size products.Taking the grinding effect coefficient as an index to distinguish different load states and grinding effects,the best load state can be achieved under the conditions of 40%filling rate,1∶37 material to ball ratio,and~6 kg sample weight.The ball mill load was evaluated using the convolutional neural network (CNN) method and optimized support vector machine(SVM) models from the acceleration signal obtained by the intelligent grinding media.For the optimized SVM models,preprocessing of the acquired one-dimensional acceleration signal,including complementary ensemble empirical mode decomposition algorithm denoising,time-domain eigenvalue extraction,and sample entropy,was conducted.The feature vectors of mill load were included in the genetic algorithm and SVM (GA-SVM),grid search and SVM (GS-SVM),and partial swarm optimization and SVM (PSO-SVM)classification models for training.The research results revealed that the recognition accuracy of the PSO-SVM algorithm reaches 98.33%,but the training process tends to be tedious and time-consuming.For the CNN algorithm with excellent applicability in the field of image recognition,the detected acceleration signal data were converted into two-dimensional pictures and directly inputted into the CNN model based on the VGG19 network for classification and recognition.The classification and recognition accuracy of the mill load of the CNN method (i.e.,98.89%) was higher than that of the optimized SVM algorithm.Moreover,the calculation time of the CNN method was shorter than that of the optimized SVM algorithm.The ball mill load status identification method using the intelligent grinding media and CNN method could provide critical solutions and technical support for load detection and online evaluation.