Applying the Anticipatory Failure Determination at a Very Early Stage of a System’S Development: Overview and Case Study
Bibliographic record
Abstract
Abstract Anticipatory Failure Determination (AFD) is a tool used in the TRIZ (Theory of Inventive Problem Solving) methodology. This article introduces its concept and describes the process of AFD in different versions of the method. The article presents the application of the AFD method at a very early state of a system’s development, i.e. its concept formulation stage, which corresponds to a technology readiness level (TRL) equal to 2. The system under analysis is a set of devices used to reduce displacement ship hull resistance. The system was modelled using functional analysis. An analysis of system resources was then carried out. Possible direct, indirect, and accident-related failures were identified. A multi-criteria analysis of the causes of system failures was conducted from which the top 10 potential failures were selected. Observations were made on the applicability of AFD in respect to systems not yet implemented.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".