Nowadays, disruption predictors, based on machine learning techniques, can perform well but they typically do not provide any information about the type of disruption and cannot predict the time remaining before the current quench. On the other hand, the automatic identification of the disruption type is a crucial aspect required to optimize the remedial actions and a prerequisite to forecasting the time left for intervening. In this work, a stack of machine learning tools is applied to the task of automatic classification of the disruption types. The strategy is implemented from scratch and completely adaptive; the predictors start operating after the first disruption and update their own models, following the evolution of the experimental program, without any human intervention. Moreover, they are designed to implement a form of transfer learning, in the sense that they identify autonomously the most important disruption classes, generating new ones when necessary. The results obtained are very encouraging in terms of both prediction performance and classification accuracy. On the other hand, regarding the narrowing of the warning times, some progress has been achieved, but new techniques will have to be devised to obtain fully satisfactory properties.

如今，基于机器学习技术的破坏预测器可以很好地运行，但是它们通常不提供有关破坏类型的任何信息，也无法预测当前淬火之前的剩余时间。另一方面，中断类型的自动识别是优化补救措施所需的关键方面，也是预测剩余干预时间的前提。在这项工作中，将一堆机器学习工具应用于中断类型的自动分类任务。该策略是从头开始实施的完全适应性强；预测器会在第一次中断后开始运行，并随着实验程序的发展而更新其自己的模型，而无需任何人工干预。而且，它们旨在实现一种形式的转移学习，从某种意义上说，它们可以自主确定最重要的中断类别，并在必要时生成新的中断类别。就预测性能和分类准确性而言，获得的结果令人鼓舞。另一方面，关于缩短警告时间，已经取得了一些进展，但是必须设计新技术以获得完全令人满意的性能。