A new reliability analysis method for repairable systems with closed‐loop feedback links
Bibliographic record
Abstract
Abstract A new reliability analysis method for repairable systems with closed‐loop feedback link (CLFL) is proposed based on GO methodology. A method for creating new function GO operators is used to describe the CLFL. Next, methods for deducing the formulae of the new GO function are proposed. In addition, a 2‐level GO model is proposed for the GO operation of repairable systems with CLFL. And then, quantitative and qualitative analysis methods for repairable systems with CLFL based on the GO method are proposed, and a process for analyzing repairable systems with CLFL based on the new GO method is formulated. Finally, we used this new GO method to analyze the reliability of an electro‐hydraulic servo speed control system and a power‐shift steering transmission control system for a heavy vehicle. To verify the feasibility, advantages, and reasonability of the new GO method, we compared our results with those obtained by fault tree analysis, Monte Carlo Simulation, and an existing GO method using serial and parallel structures to represent the CLFL. All in all, the proposed method overcomes the limitations of the existing methods as well as increasing its applicability. And it provides a new approach for reliability analysis of repairable systems with CLFL.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".