基于半监督域适应模糊推理的球磨机负荷参数软测量

Soft Sensor of Ball Mill Load Parameters Based on Semi-supervised Domain Adaptive Fuzzy Inference

针对工况改变后数据分布差异造成模型失配的问题及球磨机运行时振动信号的不确定性,研究了半监督域适应模糊推理的球磨机负荷参数软测量方法。该方法首先集成多个约束条件寻找特征变换矩阵,将待测工况和历史工况数据投影到公共子空间;接着利用模糊C均值聚类对投影后的历史数据划分规则并建立模糊推理模型。仿真实验结果表明该方法具有较高的预测精度,可以有效削弱不确定因素的影响,解决工况改变后数据分布差异造成的模型失配问题。

Aimed at the model mismatch problem caused by difference of data distribution after the change of working conditions and the influence of uncertain vibration signal when ball mill is running, a semi-supervised domain adaptation fuzzy inference method was researched. The method firstly integrates multiple constraint conditions to find the feature transformation matrix and projects the tested and historical data to the public sub-space. Then, the projected historical data is divided using fuzzy C-means clustering to establish fuzzy inference prediction model. The experiment shows that the method had high prediction accuracy, and could effectively weaken the influence of uncertain factors and solve the problem of model mismatch which is caused by data distribution difference of working condition changes.