The diagnosis of cyber-physical systems (CPS) is based on a representation of functional and faulty behaviour which is combined with system observations taken at runtime to detect faulty behaviour and reason for its root cause. In this paper we propose a scalable algorithm for an automated learning of a structured diagnosis model which -- although having a reduced size -- offers equal performance to comparable algorithms while giving better interpretability. This allows tackling challenges of diagnosing CPS: automatically learning a diagnosis model even with hugely imbalanced data, reducing the state-explosion problem when searching for a root cause, and an easy interpretability of the results. Our approach differs from existing methods in two aspects: firstly, we aim to learn a holistic global representation which is then transformed to a smaller, label-specific representation. Secondly, we focus on providing a highly interpretable model for an easy verification of the model and to facilitate repairs. We evaluated our approach on data sets relevant for our problem domain. The evaluation shows that the algorithm overcomes the mentioned problems while returning a comparable performance.

翻译：网络物理系统(CPS)的诊断基于功能和缺陷行为的描述,这种描述与在运行时为发现错误行为及其根源原因而进行的系统观测相结合,在本文中,我们提出一个可扩缩的算法,用于自动学习结构化诊断模型,该模型虽然规模缩小,但能提供与可比算法同等的性能,同时提供更好的解释性。这可以应对诊断CPS的挑战:即使数据极不平衡,也自动学习诊断模型,在寻找根本原因时减少国家爆炸问题,并易于解释结果。我们的方法与现有方法在两个方面不同:第一,我们的目标是学习一种整体的全球代表方法,然后转换成一个较小的、特定标签的表示法。第二,我们侧重于提供一种高度可解释的模式,便于对模型进行简单核查,并促进修理。我们评估了与我们问题领域相关的数据集的方法。评价表明,算法克服了上述问题,同时恢复了可比较的性能。