Research Work (current)

3. Advances in LLM-Based PLr Classification (2024–2025)

The research evolved into a detailed investigation of LLM capabilities for PLr determination and machinery safety risk assessment. This includes deterministic prompting (temperature=0), rule-based evaluation, exact/partial/no-match scoring, and multi-model benchmarking (GPT-4+, GPT-5.1-mini, Gemini models, DeepSeek, LLaMA variants).

Major publications in this direction include:

• Evaluation of Automated Machinery Functional Safety Risk Assessment Using LLMs, IEEE Access 2025

(Large-scale benchmarking study with statistical evaluation)

• Implementation of an AI-Based Expert System for Functional Safety of Machinery, Risk Analysis Journal (2025)

(Discusses prototype development of an expert system for AI-supported risk assessment without data leaving your organization)

4. Summary of Impact

This research introduces structured datasets for PLr classification, establishes reproducible benchmarking methods, develops deterministic prompting strategies, and provides scenario-level evaluation techniques for analysing LLM behaviour in machinery functional safety. It benchmarks multiple advanced AI models, examines chain-of-thought reasoning and OT security assessment, and contributes to the emergence of AI-assisted functional safety engineering as a recognised research field.

It represents one of the first systematic and reproducible examinations of how LLMs perform on machinery functional safety tasks. The work demonstrates how LLMs can support safety-critical workflows such as PLr classification, structured reasoning, and dataset-driven evaluation, while also identifying the limitations, constraints, and necessary human-in-the-loop safeguards that must be considered when applying these models in real engineering contexts.

This research direction is ongoing, with future work focusing on model reliability, deterministic reasoning, dataset expansion, and deeper integration of LLMs into early-stage safety engineering processes.

What this enables?

• Opportunities to expand dataset-driven safety analysis across broader machinery domains and new ISO/IEC standards
  
  

• Development of AI-assisted tools for hazard identification, PLr estimation, or generating safety documentation
  
  

• Exploration of hybrid human–AI workflows to enhance consistency and reduce effort in early design stages
  
  

• Joint benchmarking of next-generation LLMs for structured safety and risk-reasoning tasks
  
  

• Integration of LLM-based support tools into industrial workflows, digital engineering toolchains, and educational environments
  
  

• Collaboration on open datasets, reproducible pipelines, and evaluation frameworks for functional safety

Thank you for reading and for taking the time to explore this work.