基于多源数据特征融合的球磨机负荷软测量

针对磨矿过程球磨机负荷(ML)难以实时检测,生产中主要依靠人工经验判断负荷状态的难题,依据磨机筒体振动、振声、电流等信号与磨机负荷间存在相关性、信息互补与冗余的现象,提出基于多源数据特征融合的球磨机负荷软测量新方法.该方法由时域滤波、时频转换、特征提取、特征选择及软测量模型5部分组成.采用快速傅里叶变换(FFT)将滤波后的筒体振动及振声时域信号转换成频域信号,根据研磨机理将频域信号划分为低、中、高3个频段,采用核主元分析(KPCA)分别提取各个频段的非线性特征,选择振动、振声频域特征与电流时域特征的融合信号作为模型输入,建立基于最小二乘支持向量机(LSSVM)的磨机负荷软测量模型.实验结果表明,该方法与基于主元分析-最小二乘支持向量机(PCA-LSSVM)方法和单传感器方法相比,磨机负荷参数预测精度较高.

Soft sensor for ball mill load based on multi source data feature fusion

The real time measurement of ball mill load (ML) in grinding process is difficult to realize, and the states of ML are identified mainly by the experience of the operator. Aiming at the problems, a new soft sensor approach of ML based on the multi source data feature fusion was proposed according to the relativity, the information complementation and redundancy among shell vibration, acoustic, electricity signal and ML. The approach consisted of five parts which were data filter, time/frequency transform, feature extraction, feature selection and soft sensor model. The shell vibration and acoustic signal in the time domain was transformed into the frequency domain using fast Fourier transform (FFT). The spectral signals were partitioned into three parts which were low, medium and high frequency bands according to the grinding mechanism. The kernel principal component analysis (KPCA) was used to extract the nonlinear feature of each part. The fused signals, which consisted of the frequency domain feature of vibration and acoustic signal, and the time domain feature of electricity signal, were selected as the input variables of the soft sensor model. The soft sensor model of ML was conducted based on the least square support vector machine (LSSVM). Experimental results show that the approach has better prediction accuracy for ML parameters than the PCA LSSVM and the single sensor approaches.