Bibliographic record
Abstract
In reliability and life-testing experiments, one of the popular and commonly used strategies, that allows manufacturers and designers to identify, improve and control critical components, is called the Accelerated Life Test (ALT). The main idea of these tests is to investigate the product's reliability at higher than usual stress levels on test units to ensure earlier failure than what could result under the normal operating conditions. Stress can be induced by such factors as voltage, pressure, temperature, load or cycling rate. ALT are applied using different types of accelerations such as high usage rate in which the compressed time testing is done through speed or by reducing off times. Another type of acceleration is the product design where the life of a unit can be accelerated through its size or its geometry. Stress loading is another type of acceleration that is applied using constant stress, step-stress, progressive stress, cyclic stress or random stress. Here, we discuss the step-stress model, which applies stress to each unit and increases the stress at pre-specified times during the experiment allowing us to obtain information about the parameters of the life distribution more quickly than under normal operating conditions. In this thesis, we present the simple step-stress model (the situation in which there are only two stress levels) when the lifetimes at different stress levels follow the gamma distribution when the data are (Chapter 2) Type-II censored, (Chapter 3) Type-I censored, (Chapter 4) Progressively Type-II censored, and (Chapter 5) Progressively Type-I censored, as well as a multiple step-stress model under Type-I and Type-II censoring. The likelihood function is derived assuming a cumulative exposure model with gamma distributed lifetimes. The resulting likelihood equations do not have closed-form solutions, and so they need to be solved numerically. We then derive confidence intervals for the parameters using asymptotic normality of the maximum likelihood estimates and the parametric bootstrap method. In each case, the performances of the methods of inference developed here are examined by means of Monte Carlo simulation study and are also illustrated with some numerical examples.
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".