Ontology-Based Schema to Support Maintenance Knowledge Representation With a Case Study of a Pneumatic Valve
Bibliographic record
Abstract
This paper proposes a methodology for knowledge representation using ontology concepts. We employ an ontology-based schema to overcome the problems of heterogeneity and inconsistency in maintenance records, which are attributable to abbreviations, noisy data, nongeneric data structures, and ambiguous technical words in textual maintenance records. Our methodology employs a bond graph model (BGM) to produce a function structure of equipment related to fault propagation in part-component levels. Our method combines OWL-Lite/RDF and the ISO 14224 and ISO 15926 international standards in order to obtain a generic system-level representation model. Our approach also constructs transparent cause-effect knowledge, which facilitates interpretation and computer conversion using ISO 14224 and ISO 15926. The web ontology language (OWL) and resource description framework (RDF) are used to convert the generic human-readable interpretation into a standard computer-readable representation, thereby generating a knowledge base with maximum shareability and accessibility. We applied the methodology to a typical pneumatic valve. The results show that BGM can cross-link the identified words and the domain-specific logic to obtain the function structure of an object with causality inference, as well as enriching semantic extraction based on the context of a maintenance report, which improves the interpretation and computer conversion. Using OWL/RDF, actions such as interexchange, retrieval, and storage are possible for fault diagnosis applications in a multidisciplinary environment. Our method provides a generic technical understanding, which enriches semantic extraction and knowledge discovery in a typical maintenance report.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".