QUALITY AND PERFORMANCE RELIABILITY ASSESSMENT OF MULTI-RESPONSE DEGRADING SYSTEMS
Bibliographic record
Abstract
This paper presents a non-sampling based method for the simultaneous evaluation of quality and performance reliability of engineering systems with multiple time-variant responses due to multiple degrading components. This work provides a platform for robust design of degrading systems. The system performance degradations are related to component degradations using mechanistic models. The system soft failure is defined as the non-conformance of any response with respect to critical levels and such relations are easily modeled as time dependent limit-state functions. Then, for discrete time it is shown that an incremental failure set that emerges from a safe region can be written using only a pair of successive system instantaneous failure sets. The cumulative distribution function of soft failure is built by summing the incremental failure probabilities. A practical implementation of the proposed method can be manifest by first-order reliability methods (FORM) and second-order bounds. The proposed method can be used to assess initial quality and performance reliability of systems with combinations of designated means and tolerances. Examples of electro mechanical systems show the details of the formulation and the potential of the approach. Error sources and their magnitudes are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".