Mariëlle; id_orcid 0000-0001-6793-8165 Stoelinga

Uraz Odyurt, Ömer Sayilir, Mariëlle Stoelinga et al.

Raw datasets are often too large and unstructured to work with directly, and require a data preparation phase. The domain of industrial Cyber-Physical Systems (CPSs) is no exception, as raw data typically consists of large time-series data collections that log the system's status at regular time intervals. The processing of such raw data is often carried out using ad hoc, case-specific, one-off Python scripts, often neglecting aspects of readability, reusability, and maintainability. In practice, this can cause professionals such as data scientists to write similar data preparation scripts for each case, requiring them to do much repetitive work. We introduce CPSLint, a Domain-Specific Language (DSL) designed to support the data preparation process for industrial CPS. CPSLint raises the level of abstraction to the point where both data scientists and domain experts can perform the data preparation task. We leverage the fact that many raw data collections in the industrial CPS domain require similar actions to render them suitable for data-centric workflows. In our DSL one can express the data preparation process in just a few lines of code. CPSLint is a publicly available tool applicable for any case involving time-series data collections in need of sanitisation.

Georgiana Caltais, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga

Fault trees are a widely used as effective risk models for complex systems, answering the question "what can go wrong?", especially through minimal cut set analysis. We study fault trees from the perspective of Halpern & Pearl's theory of actual causality. This allows us to use fault trees to answer the question "why has it gone wrong?", which is fundamental to failure diagnostics. We give a complete classification of each of the different notions of actual causality in terms of the fault tree's graph structure and logical structure, and show how minimal cut sets give rise to actual causes.

Manzi Aimé Ntagengerwa, Georgiana Caltais, Mariëlle Stoelinga

The development of Cyber-Physical Systems (CPSs) is inherently multidisciplinary, involving expertise from domains such as software engineering, electrical engineering, and mechatronics. throughout the lifecycle of the system, from design to deployment. Ensuring system reliability in Cyber-Physical Systems (CPSs) requires the identification and analysis of potential failures and their cascading effects. However, reliability modeling remains a challenging and error-prone activity, as it often depends on tacit expert knowledge, incomplete documentation of failure modes, and limited consideration of interactions between subsystems. To address these challenges, this paper introduce the Capability Interaction Graph (CIG), an ontology-driven representation of CPS architectures grounded in the Unified Foundational Ontology (UFO). Due to its graph-based structure, a CIG is naturally represented as a knowledge graph (KG), enabling the explicit capture of functional dependencies and system semantics. Building upon this representation, we propose an automated synthesis algorithm for generating Fault Trees (FTs) directly from CIGs encoded as knowledge graphs. Fault Tree Analysis provides an effective mechanism for evaluating critical failure properties, including failure propagation paths and minimal cut set sets. Our approach reduces this complexity by leveraging CIGs and knowledge graphs. We provide a common semantic representation across engineering domains and support the automated generation of reliability models.