Deep multi-sequence multi-grained cascade forest for tobacco drying condition identification

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.

Keywords:

• Drying condition
• classification and identification
• multi-sequence multi-grained scanning
• deep forest

Acknowledgments

We deeply thank the reviewers and the editor for their constructive criticism of an early version of the manuscript. We also thank the whole teams of the research and development for their help of this experiment.

Disclosure statement

The authors have declared no conflict of interest.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62001262) and the Nature Science Foundation of Shandong Province (Grant No. ZR2020QF008).