Prognostics by interacting multiple model estimator
Bibliographic record
Abstract
In prognostics, the modeling of the failure models is complicated even for a single component, such as fatigue crack growth. For a complex system, there are a large number of components, and hence the failing models can be even more complicated due to diverse sub-systems and their components. The remaining useful life (RUL) of the system, as a whole, depends on many factors and there are often sudden changes in its progression pattern. The interacting multiple model (IMM) estimator is a filtering technique that tracks multiple models and reports the probability of each model. The information fusion ability of IMM with a built-in probabilistic metric is highly desirable in failure model tracking and higher level fusion. A general framework is proposed to describe the system health by a health index, then the RUL can be evaluated as the current health value divided by the degradation rate of the health index at that moment. Within the general framework of a health index, an IMM estimator is proposed to identify the failure models and evaluate both the values and the confidence interval of the RUL. Simulations on various health degradation models are carried out to illustrate the effectiveness of the IMM based RUL estimation. Specifically, the RUL sub-models can be with nearly constant degradation rate, with accelerated growing degradation rate, or some drastic break-down due to environmental changes such as a hard failure. In simulations, the truth is known, and hence the performance of the RUL estimator can be precisely assessed. This paper has not only proposed a fusion scheme to handle various failure models, but also presented the data generation procedure of health index in various situations. Such data sets can be used as benchmarks to compare various prognostics techniques.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".