Applying discrete event modeling in the real world
Bibliographic record
Abstract
There are many facets and features to applying the processes of reliability, availability, and maintainability (RAM) engineering during the lifecycle of a system. None are more important than the methodical, intentional application of modeling and simulation upfront in the design of a system to ensure that requirements are met. This paper presents and discusses solutions that demonstrate the practical application of complex modeling and how this process applies practically using real-world examples such as chemical manufacturing plants and space-borne systems. Many large manufacturing or development organizations are driven by costs of development and real-time maintenance and not seeking long term value by planning a system to be more reliable, and thus creating value by reducing cost of operation and ownership. RAM Simulation and Modeling is a process employed by RAM engineers for predicting performance of a system in order to drive value through reliability gap analysis, and project development as examples. The authors will demonstrate, through practical examples, how application of the RAM modeling has been applied to create maximum value to both Government entities and commercial companies alike. Modeling and simulation have been employed throughout all phases of the lifecycle of new system development (new plant designs, existing facilities improvements, integrated site design, spacecraft development, and maintenance task analysis) and have delivered value in the form of lower cost of operation, improved availability of the system, and value to corporate bottom lines. The authors will also demonstrate how reliability engineers have successfully provided value to design teams by helping them identify failure modes and mitigate them, thus improving the systems that they support.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".