Developing machine learning-based models to estimate time to failure for PHM
Bibliographic record
Abstract
The core of PHM (Prognostic and Health Monitoring) technology is prognostics which is able to estimate time to failure (TTF) for the monitored components or systems using the built-in predictive models. However the development of predictive models for TTF estimation remains a challenge. To address this issue, we proposed to develop machine learning-based models for TTF estimation by using the techniques from machine learning and data mining. In the past decade, we have been working on the development of machine learning-based models for estimating TTF and applied the developed technology to various real-world applications such as train wheel prognostics, and aircraft engine prognostics. In this paper, we report two kinds of machine learning-based models for estimating TTF, including multistage classification, on-demand regression. The multistage classification improves the TTF estimation over one stage classification by dividing the time window into more small narrow time windows. A case study, APU prognostics, demonstrates the usefulness of the developed methods. The results from the case study show that the machine learning-based modeling method is an effective and feasible way to develop predictive models to estimate TTF for PHM.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".