Gain more information about:
- Automating Causal Extraction
Discover how Large Language Models can identify causal relationships in engineering documentation and translate them into structured risk models. - From FMEA to Bowtie Diagrams
Learn how structured tables and narrative descriptions from FMEA documentation can be transformed into Bowtie diagrams using AI. - LLM Pipelines for Engineering Data
Explore different approaches for extracting causal information, including retrieval-augmented generation, OCR-based processing, and vision-enabled pipelines. - Prompting Strategies for Reliable Results
Understand how prompting techniques and model configurations influence the accuracy and consistency of generated Bowtie diagrams. - Challenges in AI-Assisted Failure Analysis
See where LLMs perform well—such as structured data extraction—and where further improvements are needed, particularly with unstructured engineering narratives.
What’s Inside:
- A Framework for Automating Bowtie Diagram Generation
An overview of the ACE approach for extracting causal relationships from FMEA documentation. - Evaluation of Multiple LLM Models
Insights from experiments using instruction-tuned models including LLaMA, Mistral, and Qwen. - Sensitivity Analysis of Prompting Strategies
Results showing how prompt design and strict schema constraints affect diagram quality. - Structured vs. Narrative Data Extraction
A comparison of model performance when processing structured FMEA tables versus unstructured engineering descriptions. - Future Opportunities for AI in Reliability Engineering
Key lessons and directions for applying large language models to failure analysis and risk modeling.
