Enhancing Maintenance Data Analytics: A Novel Failure Mode and Effect Analysis-Natural Language Processing Integration
Bibliographic record
Abstract
Machine learning models require considerable amounts of data, which is often a significant limitation in many real-world applications due to the scarcity of quality data. Information recorded in management systems, such as enterprise resource planning software (ERP), can nevertheless be supplemented by text fields, which can be used to enhance its quality. Natural language processing (NLP) techniques seem theoretically capable of meeting this challenge. However, the technical nature and structure of industrial text fields require further analysis to fully exploit state-of-the-art NLP models. This article presents a new methodology to leverage already available information to facilitate the implementation of NLP applications. The approach is then tested specifically in the context of reliability and maintenance, by exploiting failure modes and effects analyses (FMEA) to improve the quality of maintenance descriptions, within the framework of an NLP approach. This case study demonstrates the usefulness of the methodology, by applying it to the maintenance data of a power transmission utility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".