An Intelligent Framework of Equipment Fault Diagnosis Based on Knowledge Graph
Bibliographic record
Abstract
Equipment fault information typically exhibits the characteristics of fragmentation and diversified structure. The existing fault diagnosis methods are incapable of fully exploiting the prior knowledge and expert knowledge within the field, and the diagnosis results are overly one-sided. Given that it is challenging to obtain effective fault diagnostic knowledge for complex equipment and complex faults, this paper proposes the application of the domain knowledge graph (KG) for fault diagnosis. Different from the existing fault diagnosis methods involving the KG, our framework consists of two major parts. The first part is to construct an equipment fault KG from semi-structured and unstructured text. We further enrich the graph knowledge through knowledge completion, which can furnish high-quality knowledge sources for downstream applications. The second part is to employ the built fault KG either online or offline for fault diagnosis. We offer two approaches: the deep learning plus KG approach; the question-answering approach. The former not only guarantees the diagnostic accuracy but also provides more comprehensive diagnostic information. This constitutes the online utilization of the KG. The latter realizes the offline use of the KG, providing users with a natural and user-friendly manner to retrieve fault diagnosis information. We demonstrate and verify our proposed framework in the context of the bearing fault diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".