Updating Bayesian Network for Diagnostic Failure Analysis of Construction Equipment
Bibliographic record
Abstract
Updating Bayesian Network for Diagnostic Failure Analysis of Construction Equipment H. Q. Fan Pages 1550-1559 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Construction equipment is an important type of resources of heavy construction contractors. Since equipment breakdowns can cause project delays and significant financial losses, the contractors are eager to know those factors causing equipment failures directly or indirectly, related to equipment design, maintenance, and operations. Although Bayesian network can be used for diagnostic analysis of failure events or making predictive analysis, building a Bayesian network for such purpose can be difficult as the cause-effect relations can be subjective and their conditional probabilities change with a wide variety of causal factors. A hybrid approach is proposed in this paper to update the Bayesian diagnostic network structures and parameters using real life data, the conditional probabilities and cause-effect relationships can be dynamically updated with observed failure records to reflect the real life situations of a complex equipment system. A case study is conducted to show the benefits of the hybrid approach in construction equipment diagnostic analysis. Keywords: Construction equipment maintenance; Bayesian network learning; Failure analysis; Decision support DOI: https://doi.org/10.22260/ISARC2013/0174 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".