Integrating Reliability Engineering with Model Based Systems Engineering
Bibliographic record
Abstract
Summary & ConclusionsAs fully digital programs become commonplace, it is imperative that Reliability Engineering (RE) adapt to take advantage of all the benefits that Model-Based Systems Engineering (MBSE) offers. At the same time, integrating RE with MBSE cannot result in a loss of modeling and prediction fidelity, nor should it drive significant learning on reliability engineers of new tools and programming languages. The approach should, however, consider the particular nuances of the modeling being performed. This paper describes two distinct techniques for this integration effort adopted by two program areas within the same company. Both approaches contain unique advantages, and it is shown that there is no single best way to perform RE within an MBSE framework.The first approach described performs the reliability analysis in a Microsoft Excel based model. The important properties of the system hardware are captured in a table in Cameo. This table is then exported to Excel and compared to the hardware in the Excel reliability model. Once any hardware changes have been updated, the table can be exported back to Cameo to include the updated reliability properties.The second approach described integrates MATLAB with Cameo. The reliability analysis is either performed in MATLAB or another tool then integrated into MATLAB. Then, through a series of scripts, Cameo connects with the MATLAB analysis to fully integrate the system model with the reliability model.Each of these approaches can be used by a wide variety of programs in diverse industries. The appeal of these approaches is that it allows Reliability Engineers to continue using their current tools while ensuring that their models always align with the current system architecture.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".