A probabilistic approach to remaining useful life prediction of rolling element bearings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we present a probabilistic approach for fault detection and prognosis of rolling element bearings based on a two-phase degradation model. One of the main issues in dealing with bearing degradation is that the degradation mechanism is unobservable and can only be inferred through appropriate surrogate measures obtained from indirect sensory measurements. Furthermore, the stochastic nature of the degradation path renders fault detection and estimating the end-of-life characteristics from such data extremely challenging. When such components are a part of a larger system, the exact degradation path depends on both the operating and loading conditions, which means that the most effective condition monitoring approach should estimate the degradation model parameters under operational conditions, and not solely from isolated component testing or historical information. Motivated by these challenges, a two-phase degradation model using surrogate measures of degradation from vibration measurements is proposed and a Bayesian approach is used to estimate the model parameters. The underlying methodology involves using priors from historical data, while the posterior calculations are undertaken using surrogate measures obtained from a monitored unit combined with the aforesaid priors. The problem of fault detection is posed as a change point location problem. This allows the prior knowledge obtained from the past failures to be integrated for maintenance planning of a currently working unit in a systematic way. The correlation between the degradation rate and the time of occurrence of the change point, an often overlooked aspect in prognosis, is also considered in here. A numerical example and a case study are presented to illustrate the overall methodology and the results obtained using this approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it