一句话：提出基于重要性加权的漂移处理方法，使工业ML软件在新增熔炉时实现自适应并提升性能。
论文关注真实制造中可扩展工业过程对机器学习（ML）软件自我适应的需求：当生产条件变化或新增设备时，软件需自动调整。作者以Uddeholms AB钢铁公司的电渣重熔（ESR）过程为案例，任务为预测真空抽气事件的最低压力值。考虑到新记录采集周期长且新机器需高效集成到既有ML软件，论文提出一种自动化自适应策略，采用重要性加权的漂移处理技术，以支持新增熔炉投产并满足可适应性非功能需求。评估将该方法与经典非自适应方法对比；结果表明，所提方法提升了ML软件的性能。
Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learning (ML) software systems to the new conditions. In this paper, we investigate an Electroslag Remelting (ESR) use case process from the Uddeholms AB steel company. The use case involves predicting the minimum pressure value for a vacuum pumping event. Taking into account the long time required to collect new records and efficiently integrate the new machines with the built ML software system. Additionally, to accommodate the changes and satisfy the non-functional requirement of the software system, namely adaptability, we propose an automated and adaptive approach based on a drift handling technique called importance weighting. The aim is to address the problem of adding a new furnace to production and enable the adaptability attribute of the ML software. The overall results demonstrate the improvements in ML software performance achieved by implementing the proposed approach over the classical non-adaptive approach.
@inproceedings{paperbot7298,
title = {A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes},
author = {Firas Bayram and Bestoun S. Ahmed and Erik Hallin and Anton Engman},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559495}
}