球磨机信号分析和关键参数预报系统

Abstract

为了实现磨矿过程球磨机负荷参数的在线检测，采用MATLAB和C#.net混合编程方式，开发实现了球磨机振动、振声信号分析和关键参数预报的软测量系统。该系统基于集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)技术和区间偏最小二乘(interval partial least-squares, iPLS)技术提取与磨机负荷参数密切相关的本征模态函数(intrinsic mode functions, IMF)频域特征，构建基于本征模态函数特征空间的选择性集成模型，实现磨机负荷参数的测量。MATLAB的Deploytool工具将信号EEMD分解、IMF频谱变换、iPLS特征选择、关键参数模型训练和模型预测一系列m函数编译生成DLL程序集合，在C#.net编程环境中，通过调用上述程序实现球磨机信号分析和关键参数预报软件系统的快速开发。系统测试结果表明该系统能够有效选择筒体振动和振声信号IMF频谱特征，系统准确性和可靠性较高，对改进磨矿过程控制和优化具有重要意义。 Due to the difficult of the ball mill load to measure directly, we combined matlab language with c#.net programming to develop a signal analysis and key parameters prediction system of ball mill. The vibration and acoustic signals are decomposed a series of Intrinsic Mode Functions (IMFs) by using Ensemble Empirical Mode Decomposition (EEMD). The frequency domain characteristics of the IMFs were chosen based on interval Partial Least-Squares (iPLS) and forecast model of key load parameters were built based on extreme learning machine ensemble modeling. In order to solve the problem of monitoring and estimating the critical parameters of the ball mill’s running state. Use the Deploytool tool of Matlab to compile a series of M function of signal EEMD decomposition, IMF transform spectrum, iPLS feature selection and the model training and prediction of the key parameters into C#.NET DLL assemblies, and then call them in C#. The software system of signal analysis and key parameters prediction of ball mill is developed, which has the function of signal decomposition, feature selection, model training and parameter prediction. System test results show that the system can effectively select the cylinder vibration and noise signal IMF spectrum characteristics closely related to the ratio of material ball, grinding concentration and filling rate. In addition, the mill load parameters forecasting system based on spectral characteristics has good generalization performance.