In the cold rolling process of steel strip products, strip breakage is an undesired production failure which can lead to yield loss, reduced work speed and equipment damage. To perform a root cause analysis, conventional physics-based approaches which focus on mechanical and metallurgical principles have been applied in a retrospective manner. With the advancement of data acquisition technologies, numerous process monitoring data is collected by various sensors deployed along this process; however, conventional approaches cannot take advantage of these data. In this paper, a machine learning-based approach is proposed to characterise and model strip breakage in a predictive manner. First, to match the temporal characteristic of strip breakage which occurs instantaneously, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from three facets – physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using recurrent neural networks (RNNs), which are specialised in discovering underlying patterns embedded in time-series data. An experimental study using real-world data collected from a cold-rolled electrical steel strip manufacturer revealed the effectiveness of the proposed approach.

在钢带产品的冷轧过程中，断带是一种不希望的生产故障，会导致产量损失、工作速度降低和设备损坏。为了进行根本原因分析，以回顾性方式应用了关注机械和冶金原理的传统基于物理学的方法。随着数据采集技术的进步，在这个过程中部署的各种传感器收集了大量的过程监控数据；然而，传统方法无法利用这些数据。在本文中，提出了一种基于机器学习的方法来以预测的方式表征和模拟带钢断裂。首先，为了匹配瞬间发生的带钢断裂的时间特性，以run-to-failure的方式提取冷轧过程的历史多元时间序列数据，并采用滑动窗口策略进行数据标注。其次，从三个方面确定了以破损为中心的特征——基于物理的方法、经验知识和数据驱动的特征。最后，这些特征被用作使用循环神经网络 (RNN) 进行条带断裂建模的输入，循环神经网络专门用于发现嵌入在时间序列数据中的潜在模式。使用从冷轧电工钢带制造商收集的真实数据进行的实验研究揭示了所提出方法的有效性。从三个方面确定了以破损为中心的特征——基于物理的方法、经验知识和数据驱动的特征。最后，这些特征被用作使用循环神经网络 (RNN) 进行条带断裂建模的输入，循环神经网络专门用于发现嵌入在时间序列数据中的潜在模式。使用从冷轧电工钢带制造商收集的真实数据进行的实验研究揭示了所提出方法的有效性。从三个方面确定了以破损为中心的特征——基于物理的方法、经验知识和数据驱动的特征。最后，这些特征被用作使用循环神经网络 (RNN) 进行条带断裂建模的输入，循环神经网络专门用于发现嵌入在时间序列数据中的潜在模式。使用从冷轧电工钢带制造商收集的真实数据进行的实验研究揭示了所提出方法的有效性。