# AI- and Ontology-Based Enhancements to FMEA for Advanced Systems Engineering: Current Developments and Future Directions

arXiv: https://arxiv.org/abs/2511.17743 · HTML: https://mlsanity.com/paper/2511.17743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 181 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024)
- [DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineering](https://mlsanity.com/paper/2308.01597/) (2023)
- [Are "Non-functional" Requirements really Non-functional?](https://mlsanity.com/paper/1611.08868/) (2016)
- [Design Ontology Supporting Model-based Systems-engineering Formalisms](https://mlsanity.com/paper/2010.07627/) (2020)
- [Combining Machine Learning and Ontology: A Systematic Literature Review](https://mlsanity.com/paper/2401.07744/) (2024)
- [Driving Digital Engineering Integration and Interoperability Through Semantic Integration of Models with Ontologies](https://mlsanity.com/paper/2206.10454/) (2022)

## Abstract (arXiv)

This article presents a state-of-the-art review of recent advances aimed at transforming traditional Failure Mode and Effects Analysis (FMEA) into a more intelligent, data-driven, and semantically enriched process. As engineered systems grow in complexity, conventional FMEA methods, largely manual, document-centric, and expert-dependent, have become increasingly inadequate for addressing the demands of modern systems engineering. We examine how techniques from Artificial Intelligence (AI), including machine learning and natural language processing, can transform FMEA into a more dynamic, data-driven, intelligent, and model-integrated process by automating failure prediction, prioritisation, and knowledge extraction from operational data. In parallel, we explore the role of ontologies in formalising system knowledge, supporting semantic reasoning, improving traceability, and enabling cross-domain interoperability. The review also synthesises emerging hybrid approaches, such as ontology-informed learning and large language model integration, which further enhance explainability and automation. These developments are discussed within the broader context of Model-Based Systems Engineering (MBSE) and function modelling, showing how AI and ontologies can support more adaptive and resilient FMEA workflows. We critically analyse a range of tools, case studies, and integration strategies, while identifying key challenges related to data quality, explainability, standardisation, and interdisciplinary adoption. By leveraging AI, systems engineering, and knowledge representation using ontologies, this review offers a structured roadmap for embedding FMEA within intelligent, knowledge-rich engineering environments.