A Bayesian networks approach to fleet availability analysis considering managerial and complex causal factors
Bibliographic record
Abstract
Availability analysis of a fleet of assets requires modelling uncertainty sources that affect equipment reliability and maintainability. These uncertainties include complex, managerial causalities and risks which have been seldom examined in the asset management literature. The objective of this study is to measure the reliability, maintainability and availability of a fleet, considering the effect of common causal factors and extremely rare or previously unobserved events. We develop a fully probabilistic availability analysis model using hybrid Bayesian networks (BNs), to capture managerial, organisational and environmental causal factors that influence failure or repair rate, as well as those that affect both failure and repair rates simultaneously. The proposed methodology has been found more accurate in forecasting failure rate, repair rate, and average availability level of a fleet of assets, providing asset managers with an inference mechanism to not only measure the performance of the assets based on common causal factors, but also learn the actual level of such factors and thereby identify improvement areas. We have demonstrated the application of the model using a fleet of excavators located in Toronto, Ontario. The prediction accuracy of the proposed model is evaluated by use of a measure of prediction error. [Received: 19 March 2019; Accepted: 3 September 2019]
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".