AI-driven FMEA: integration of large language models for faster and more accurate risk analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Failure mode and effects analysis (FMEA) is a critical but labor-intensive process in product development that aims to identify and mitigate potential failure modes to ensure product quality and reliability. In this paper, a novel framework to improve the FMEA process by integrating generative artificial intelligence (AI), in particular large language models (LLMs), is presented. By using these advanced AI tools, we aim to streamline collaborative work in FMEA, reduce manual effort and improve the accuracy of risk assessments. The proposed framework includes LLMs to support data collection, pre-processing, risk identification, and decision-making in FMEA. This integration enables a more efficient and reliable analysis process and leverages the strengths of human expertise and AI capabilities. To validate the framework, we conducted a case study where we first used GPT-3.5 as a proof of concept, followed by a comparison of the performance of three well-known LLMs: GPT-4, GPT-4o and Gemini. These comparisons show significant improvements in terms of speed, accuracy, and reliability of FMEA results compared to traditional methods. Our results emphasize the transformative potential of LLMs in FMEA processes and contribute to more robust design and quality assurance practices. The paper concludes with recommendations for future research focusing on data security and the development of domain-specific LLM training protocols.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it