Reliability sequential compliance method for a partially observable gear system subject to vibration monitoring
Bibliographic record
Abstract
Assumptions accompanying exponential failure models are often not met in the standard sequential probability ratio test (SPRT) of many products. However, for most of the mechanical products, Weibull distribution conforms to their life distributions better compared to other techniques. The SPRT method for Weibull life distribution is derived in this paper, which enables the implementation of reliability compliance tests for gearboxes. Using historical failure data and condition monitoring data, a life prediction model based on hidden Markov model (HMM) is established to describe the deterioration process of gearboxes, then the predicted remaining useful life (RUL) is transformed into failure data that is used in SPRT for further analysis, which can significantly save on testing time and reduce costs. Explicit expression for the distribution of RUL is derived in terms of the posterior probability that the system is in the unhealthy state. The predicted and actual values of the residual life are compared, and the average relative error is 3.90 %, which verifies the validity of the proposed residual life prediction approach. A comparison with other life prediction and SPRT methods is given to elucidate the efficacy of the proposed approach.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".