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

Haytham Younus, Sohag Kabir, Felician Campean, Pascal Bonnaud, David Delaux

TL;DR

The paper addresses the limitations of traditional, manual FMEA in handling modern, complex systems and argues for a knowledge-driven approach grounded in AI, ontologies, and MBSE. It surveys AI techniques for failure prediction, prioritisation, and textual data mining, and couples them with function modelling and ontological knowledge representation to enable semantic reasoning and traceability. Key contributions include a critical analysis of FMEA evolution, integration of MBSE with function modelling, ontology-based modelling of FMEA artefacts, and a synthesis of hybrid AI–ontology strategies with a roadmap of tools and platforms. The work demonstrates that AI and ontologies can deliver explainable, adaptive, and scalable reliability analysis, offering practical pathways for embedding FMEA within intelligent systems engineering environments and improving cross-domain collaboration and lifecycle decision-making.

Abstract

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.

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

TL;DR

The paper addresses the limitations of traditional, manual FMEA in handling modern, complex systems and argues for a knowledge-driven approach grounded in AI, ontologies, and MBSE. It surveys AI techniques for failure prediction, prioritisation, and textual data mining, and couples them with function modelling and ontological knowledge representation to enable semantic reasoning and traceability. Key contributions include a critical analysis of FMEA evolution, integration of MBSE with function modelling, ontology-based modelling of FMEA artefacts, and a synthesis of hybrid AI–ontology strategies with a roadmap of tools and platforms. The work demonstrates that AI and ontologies can deliver explainable, adaptive, and scalable reliability analysis, offering practical pathways for embedding FMEA within intelligent systems engineering environments and improving cross-domain collaboration and lifecycle decision-making.

Abstract

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.

Paper Structure

This paper contains 33 sections, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Foundations of FMEA-Based Systems Engineering
  3. Overview of FMEA in Design and Reliability Engineering
  4. Standards, Practices, Tools, and Limitations
  5. Role of FMEA in System-Level Risk Management
  6. Need for Evolution to Address Complexity, Traceability, and Adaptability Issues
  7. Critical Review of Enhancement of FMEA with AI Techniques
  8. AI Methods for Failure Prediction and Mode Classification
  9. Machine Learning for Automated FMEA Prioritisation
  10. Natural Language Processing for Analysing Failure Records and Maintenance Logs
  11. Benefits and Challenges of AI Integration in FMEA
  12. Function Modelling and Model-Based Systems Engineering (MBSE)
  13. Foundations of Function Modelling and Behaviour in Systems Engineering
  14. FBS and Alternative Frameworks in Conceptual Design
  15. From Document-Centric to Model-Based Systems Engineering
  16. ...and 18 more sections

Figures (6)

  • Figure 1: Thematic structure of the review
  • Figure 2: Example row from a Design FMEA Table for a Window Lifter System, adapted from handbook2019failure
  • Figure 3: Cause-and-effect chain linking system-level failures to lower-level root causes, adapted from handbook2019failure
  • Figure 4: The FBS Framework: From Function to Behaviour to Structure (Adapted from Eisenbart2020Function)
  • Figure 5: The four-step FMA framework developed by BEQIC for integrated failure management Henshall2015InterfaceAnalysis
  • ...and 1 more figures