An Analysis of Early-Stage Functional Safety Analysis Methods and Their Integration into Model-Based Systems Engineering

• URL: http://arxiv.org/abs/2511.02874v1
• Date: Tue, 04 Nov 2025 05:38:55 GMT
• Title: An Analysis of Early-Stage Functional Safety Analysis Methods and Their Integration into Model-Based Systems Engineering
• Authors: Jannatul Shefa, Taylan G. Topcu,
• Abstract summary: This paper investigates the capabilities of key safety analysis techniques in terms of their integration into Model-Based Systems Engineering (MBSE)<n>We find that MBSE integration efforts primarily focus on Failure Mode and Effects Analysis (FMEA), and integration of FHA and FFIP is nascent.<n>While our findings indicate a variety of MBSE integration approaches, there is no universally established framework or standard.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As systems become increasingly complex, conducting effective safety analysis in the earlier phases of a system's lifecycle is essential to identify and mitigate risks before they escalate. To that end, this paper investigates the capabilities of key safety analysis techniques, namely: Failure Mode and Effects Analysis (FMEA), Functional Hazard Analysis (FHA), and Functional Failure Identification and Propagation (FFIP), along with the current state of the literature in terms of their integration into Model-Based Systems Engineering (MBSE). A two-phase approach is adopted. The first phase is focused on contrasting FMEA, FHA, and FFIP techniques, examining their procedures, along with a documentation of their relative strengths and limitations. Our analysis highlights FFIP's capability in identifying emergent system behaviors, second-order effects, and fault propagation; thus, suggesting it is better suited for the safety needs of modern interconnected systems. Second, we review the existing research on the efforts to integrate each of these methods into MBSE. We find that MBSE integration efforts primarily focus on FMEA, and integration of FHA and FFIP is nascent. Additionally, FMEA-MBSE integration efforts could be organized into four categories: model-to-model transformation, use of external customized algorithms, built-in MBSE packages, and manual use of standard MBSE diagrams. While our findings indicate a variety of MBSE integration approaches, there is no universally established framework or standard. This leaves room for an integration approach that could support the ongoing Digital Engineering transformation efforts by enabling a more synergistic lifecycle safety management methods and tools.

Related papers

• A Method for Quantifying Human Risk and a Blueprint for LLM Integration [0.0]
  The Cybersecurity Psychology Framework (CPF) is a novel methodology for quantifying human-centric vulnerabilities in security operations.<n>CPF provides end-to-end operationalization across the full spectrum of psychological vulnerabilities.
  arXiv  Detail & Related papers  (2025-09-29T20:31:27Z)
• HySafe-AI: Hybrid Safety Architectural Analysis Framework for AI Systems: A Case Study [5.447634497206096]
  AI has become integral to safety-critical areas like autonomous driving systems (ADS) and robotics.<n>In this paper, we review different architectural solutions and then evaluate the efficacy of common safety analyses.<n>We introduce HySAFE-AI, a hybrid framework that adapts traditional methods to evaluate the safety of AI systems.
  arXiv  Detail & Related papers  (2025-07-23T01:41:51Z)
• BlueGlass: A Framework for Composite AI Safety [0.2999888908665658]
  This paper introduces BlueGlass, a framework designed to facilitate AI safety by providing a unified infrastructure.<n>To demonstrate the utility of this framework, we present three safety-oriented analyses on vision-language evaluation.<n>More broadly, this work contributes infrastructure and findings for building more robust and reliable AI systems.
  arXiv  Detail & Related papers  (2025-07-14T09:45:34Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• UniSTPA: A Safety Analysis Framework for End-to-End Autonomous Driving [10.063740202765343]
  We propose the Unified System Theoretic Process Analysis (UniSTPA) framework.<n>UniSTPA performs hazard analysis not only at the component level but also within the model's internal layers.<n>The proposed framework thus offers both theoretical and practical guidance for the safe development and deployment of end-to-end autonomous driving systems.
  arXiv  Detail & Related papers  (2025-05-21T01:23:31Z)
• SoK: The Security-Safety Continuum of Multimodal Foundation Models through Information Flow and Game-Theoretic Defenses [58.93030774141753]
  Multimodal foundation models (MFMs) integrate diverse data modalities to support complex and wide-ranging tasks.<n>In this paper, we unify the concepts of safety and security in the context of MFMs by identifying critical threats that arise from both model behavior and system-level interactions.
  arXiv  Detail & Related papers  (2024-11-17T23:06:20Z)
• Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting [87.37278888311839]
  Iterative Markovian Fitting (IMF) procedure successfully solves the Schr"odinger Bridge (SB) problem.<n>We show a close connection between IMF and the Iterative Proportional Fitting (IPF) procedure.<n>We refer to this combined approach as the Iterative Proportional Markovian Fitting (IPMF) procedure.
  arXiv  Detail & Related papers  (2024-10-03T15:43:17Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.