failure modes

This study addresses safety risks arising from software faults in cyber-physical systems (CPS) for electric bicycles. We propose a simulation-driven functional Failure Mode and Effects Analysis (FMEA) method, leveraging Simulink Fault Analyzer to construct fault models, integrated with expert review and a systematic FMEA process to close the loop among fault modeling, simulation-based analysis, and impact assessment. Experimental evaluation identified 13 real-world faults with 100% model accuracy; among them, five revealed previously unrecognized safety implications, and 38.4% induced anomalous system behavior. The study distills ten reusable engineering practice guidelines, significantly enhancing the effectiveness and practicality of FMEA in industrial-scale CPS. It provides empirical validation and methodological contributions toward the operational deployment of simulation-driven safety analysis.

This paper addresses the insufficient integration of early-system safety analysis with Model-Based Systems Engineering (MBSE). It comparatively evaluates three functional safety analysis methods—Failure Mode and Effects Analysis (FMEA), Functional Hazard Assessment (FHA), and Fault Feedback and Impact Propagation (FFIP)—and identifies FFIP as superior for detecting emergent behaviors, second-order effects, and fault propagation. Subsequently, it systematically reviews existing MBSE integration practices, categorizing them into four approaches: model transformation, custom algorithm development, built-in toolkits, and manual modeling. The study reveals that current integration efforts are predominantly focused on FMEA, while FHA and FFIP remain in exploratory stages, hindered by the absence of a unified framework and standardized guidelines. To bridge this gap, the paper proposes a novel, full-lifecycle safety analysis integration paradigm aligned with digital engineering transformation—enabling traceable, executable, and evolvable model-driven safety verification.

In complex equipment fault diagnosis, domain expertise is difficult to formalize, and manual fault tree construction is inefficient. Method: This paper proposes a knowledge graph–based approach for automated fault tree synthesis. It introduces a lightweight, semantically rich knowledge graph representation that enables semi-automatic extraction of failure logic relationships from unstructured documents (e.g., maintenance manuals) and structured/functional models. Leveraging hierarchical modeling and semantic reasoning, the method generates fault trees in a fully structured manner—without requiring historical fault data, relying solely on engineering knowledge. Contribution/Results: The synthesized fault trees are inherently interpretable and accurately capture system-level failure propagation paths. Experimental validation on the Lycoming O-320 aircraft engine demonstrates substantial improvements in diagnostic modeling efficiency and engineering applicability.

Traditional Failure Mode and Effects Analysis (FMEA) development for industrial equipment is highly manual, time-consuming, and suffers from low knowledge reuse. Method: This paper proposes a foundation-model-based approach for automated FMEA generation and structured database ingestion. It integrates domain-adapted natural language processing and information extraction to accurately identify fault modes, effects, causes, and detection mechanisms from unstructured technical documents, mapping them to standardized FMEA table entries. An interpretable, interactive correction mechanism enables expert feedback integration for iterative refinement, while structured outputs are automatically persisted into a relational database. Contribution/Results: Experiments demonstrate over 80% reduction in FMEA development cycle time, significantly improving efficiency and consistency in industrial asset knowledge construction. The approach validates the feasibility and practical value of foundation models in high-reliability industrial knowledge engineering applications.

This study addresses the challenge of effectively monitoring early-stage agent systems, where structural flaws often obscure task-level errors. The authors propose a three-dimensional (quality, suitability, efficiency) and three-granularity (intra-run, inter-run, structural) monitoring and triaging framework tailored for low-maturity agent systems. They introduce a novel system maturity staging model based on the coefficient of variation and monitoring granularity, integrated with a severity classification adapted from FMEA to guide human review. The resulting transferable monitoring architecture supports document-driven, multi-stage workflows, enhanced by a synthetic testbed with controlled error injection. Experimental results demonstrate that structural defects significantly mask task-level signals; 97% of issues can be automatically traced, with only 2% requiring human intervention, and each granularity level precisely identifies its corresponding defect type (coefficients of variation: 0.02, 1.25, and 0.00, respectively).

Traditional FMEA suffers from heavy manual effort, poor knowledge reusability, and limited cross-domain interoperability, hindering its applicability to complex systems engineering. This paper proposes a semantic-enhanced intelligent FMEA framework that integrates ontology-based modeling with large language models (LLMs) to construct a domain-specific knowledge graph, enabling formal representation and explainable reasoning of failure knowledge. It combines machine learning and natural language processing for automated fault identification, failure propagation analysis, and dynamic risk prioritization. Furthermore, the framework leverages Model-Based Systems Engineering (MBSE) and functional modeling to enable adaptive reconstruction of the FMEA workflow. Evaluated across multiple industrial case studies, the framework achieves an average 23.6% improvement in fault identification accuracy and reduces analysis time by approximately 40%. It establishes a scalable, verifiable, and knowledge-driven paradigm for intelligent FMEA implementation in knowledge-intensive systems engineering.

Predictive maintenance in manufacturing often relies on spurious correlations, hindering identification of true causal mechanisms underlying equipment failures and leading to misdiagnosis and inefficient interventions. To address this, we propose a causal machine learning–based decision framework that leverages a pre-trained causal foundation model as a “what-if” reasoning engine to systematically identify root causes and quantify their causal effects on Overall Equipment Effectiveness (OEE). Our method integrates causal inference modeling with intervention-effect estimation on semi-synthetic data, enabling interpretable and actionable ranking and recommendation of maintenance strategies. Experiments demonstrate that, compared to conventional predictive models, our framework significantly improves the accuracy of identifying effective interventions, increases average OEE by 12.3%, and reduces unnecessary downtime by 37.6%. This represents a critical transition from failure prediction to causally grounded, proactive operational optimization.

This study addresses the limitations of traditional statistical fault localization (SFL), which relies solely on code execution traces and often fails to accurately pinpoint root causes. To overcome this, the authors systematically incorporate execution features—such as data flow, variable values, and branch conditions—extracted via the EFDD tool from the Tests4Py dataset. They train project-specific random forest models and map feature importance back to source code lines, integrating these insights with classical SFL formulas to enhance localization accuracy. Rigorous evaluation is conducted using a confounder-adjusted mixed-effects model and paired statistical tests. Experimental results demonstrate that the proposed approach significantly improves the accuracy of reference patches while reducing inspection effort at both line and function levels, confirming its robustness and practicality across multiple dimensions.

This work addresses the limitation of traditional Failure Modes and Effects Analysis (FMEA) in automotive semiconductors, which focuses solely on functional safety while neglecting the synergistic vulnerabilities and common-cause consequences arising from interactions with cybersecurity. To bridge this gap, the authors propose a unified Functional Safety and Cybersecurity Threat and Risk Analysis (FTMEA) framework that introduces, for the first time, quantifiable Cross-Domain Correlation Factors (CDCFs). These CDCFs integrate expert knowledge, static structural analysis (e.g., controllability and observability), and empirical data from fault and attack injection experiments to enable a cohesive risk modeling and prioritization mechanism. Applied to an automotive ASIC configuration register case study, the approach successfully identifies cross-domain risks overlooked by conventional FMEA and TARA, significantly enhancing the effectiveness of mitigation strategies and providing traceable, quantifiable risk assessment evidence.

This work addresses the limitations of traditional Failure Modes, Effects, and Diagnostic Analysis (FMEDA) in automotive ASIC functional safety verification, where expert judgment is used to estimate failure mode distributions and diagnostic coverage without quantifying associated uncertainties, thereby compromising reliability. For the first time, error propagation theory is systematically integrated into FMEDA to construct uncertainty models for both failure mode distributions and diagnostic coverage. This enables quantitative computation of the maximum deviations and confidence intervals for the Single-Point Fault Metric (SPFM) and Latent Fault Metric (LFM). Furthermore, an Error Importance Indicator (EII) is introduced to trace the key contributors driving overall uncertainty. The proposed approach significantly enhances the transparency and credibility of FMEDA, offering a scientifically rigorous and quantifiable foundation for compliance with ISO 26262.