Abstract

The term, “drying condition” refers to the actual dehydration capacity of a rotary dryer in the tobacco drying process which is directly related to the drying effect. However, identifying different drying conditions relies heavily on the judgment of field engineers who have rich domain knowledge and practical experiences. In this study, we proposed an improved multi-sequence multi-grained cascade forest model, MSgcForest, to classify and identify different drying conditions. An improved multi-sequence multi-grained feature scanning mechanism is added to perform spacial and sequential feature extraction from raw production-related data, which transforms the input features into high-dimensional feature vectors and increases the discriminative power of the drying condition features. Comparison with existing models indicates that the proposed MSgcForest outperforms the other alternatives even for small-scale training data. In particular, this method successfully transforms the fuzzy artificial judgment of the drying condition into a data-driven identification with high precision, which provides a promising prospect for identifying working conditions in industrial processes.

摘要

术语“干燥条件”是指旋转干燥机在烟草干燥过程中的实际脱水能力，直接关系到干燥效果。然而，识别不同的干燥条件在很大程度上依赖于具有丰富领域知识和实践经验的现场工程师的判断。在这项研究中，我们提出了一种改进的多序列多粒度级联森林模型 MSgcForest，用于对不同的干燥条件进行分类和识别。增加了改进的多序列多粒度特征扫描机制，对原始生产相关数据进行空间和序列特征提取，将输入特征转化为高维特征向量，提高干燥条件特征的判别力。与现有模型的比较表明，即使对于小规模的训练数据，所提出的 MSgcForest 也优于其他替代方案。特别是该方法成功地将干燥工况的模糊人工判断转化为数据驱动的高精度识别，为工业过程中的工况识别提供了广阔的前景。