融合时/频信息的磨矿过程磨机负荷软测量

磨机负荷(ML)是磨矿过程的重要参数, 能否准确地确定ML状态及ML参数直接影响磨矿产品的质量、产量及设备安全. 针对实际生产中只能依据专家经验判断ML状态, 难以检测与ML及ML状态直接相关的ML参数的问题, 本文提出了融合时频信息的ML软测量策略和相应的软测量方法. 该方法首先求取磨机筒体振动及振声信号的频谱, 再采用自适应遗传算法—偏最小二乘(AGA--PLS)选择频谱特征, 然后融合时域电流信号, 基于PLS算法建立融合时频数据特征的ML参数检测模型, 最后通过规则推理模型判别ML状态. 通过实验球磨机的磨矿过程验证了该软测量方法的有效性.

Soft sensing mill load in grinding process by time/frequency information fusion

Mill load (ML) is a key parameter of grinding process. Whether the status of ML and the parameters of ML can be accurately identified affects the quality and quantity of the product, and the safety of the grinding equipment. In practice, the ML status is monitored by the experience of the experienced operators. The ML parameters relate to ML and ML status directly, which is difficulty to be measured. To deal with these problems, a soft sensor strategy and an approach based on time/frequency information fusion are proposed. In this approach, at first the power spectrum of the shell vibration and acoustical signals are obtained. Then, the frequency spectrum features are selected by using adaptive genetic algorithm-partial least squares (AGA--PLS). These frequency spectrum features are fused with the current signal of the mill motor, constituting the PLS--based model for predicting the ML parameters. Finally the ML status is obtained by the ruler reasoning-based discrimination model. A grinding process experiment in the laboratory-scale ball mill validates the efficacy of the proposed soft sensor approach.