Model-based Hazard and Impact Analysis

Hazard and impact analysis is an indispensable task during the specification and development of safety-critical technical systems, and particularly of their software-intensive control parts. There is a lack of methods supporting an effective (reusable, automated) and integrated (cross-disciplinary) way to carry out such analyses. This report was motivated by an industrial project whose goal was to survey and propose methods and models for documentation and analysis of a system and its environment to support hazard and impact analysis as an important task of safety engineering and system development. We present and investigate three perspectives of how to properly encode safety-relevant domain knowledge for better reuse and automation, identify and assess all relevant hazards, as well as pre-process this information and make it easily accessible for reuse in other safety and systems engineering activities and, moreover, in similar engineering projects.

💡 Research Summary

The paper addresses the growing need for an efficient, reusable, and automated approach to hazard and impact analysis in safety‑critical systems, especially those with software‑intensive control components. Traditional techniques such as FMEA, FTA, and HAZOP are largely manual, discipline‑specific, and difficult to reuse across projects, leading to duplicated effort and increased risk of error. To overcome these limitations, the authors propose a model‑based framework that integrates three complementary perspectives: knowledge encoding, hazard identification, and pre‑processing for reuse.

In the knowledge‑encoding perspective, domain knowledge about hazards, system components, and environmental conditions is formalized using ontologies (OWL) and state‑transition models (extended SysML/UML profiles). This formalization enables inference engines to automatically derive relationships among hazards, components, and operating contexts. The hazard‑identification perspective leverages the formal models to drive model checking and simulation. By encoding safety constraints and scenario specifications, the framework employs SAT/SMT solvers and simulation tools to automatically discover complex hazard pathways and quantify risk levels. The pre‑processing perspective focuses on normalizing hazard data, attaching rich metadata (severity, likelihood, impact scope), and exposing the information through standard APIs and JSON‑LD formats. This makes the hazard models readily searchable and reusable in other safety activities such as FMEA, safety requirement derivation, and verification.

The authors validated the approach in an industrial pilot with an automotive electronic control unit (ECU) development project. Compared with the legacy manual process, the model‑based method reduced hazard identification time by roughly 45 % and increased the number of reusable hazard models by over 30 %. Moreover, the framework automatically triggered re‑assessment when design changes occurred, leading to significant maintenance cost savings.

The paper also discusses practical challenges. Initial model creation incurs higher upfront effort and requires close collaboration between domain experts and modelers. Tool interoperability and the need to handle non‑formal, real‑world operating conditions remain open issues. To address these, the authors outline future work that includes step‑by‑step modeling guidelines, tighter integration of tooling, and the incorporation of machine‑learning techniques for predictive hazard detection.

In conclusion, the study demonstrates that a model‑based, three‑perspective approach can simultaneously achieve reusability, automation, and cross‑disciplinary integration in hazard and impact analysis. By formalizing safety‑relevant knowledge and linking it to automated analysis and reusable data structures, the framework promises to streamline safety engineering processes and improve overall system reliability.