Multivariate Phase Space Warping-Based Degradation Tracking and Remaining Useful Life Prediction of Rolling 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
Effective utilization of signals collected by distributed sensor networks is crucial for tracking degradation and forecasting the remaining useful life (RUL) of rolling bearings. The phase space warping (PSW) algorithm constructs the hierarchical dynamics to physically describe damage evolution. However, the PSW algorithm is unable to handle multivariate signals. To enable synchronous tracking of degradation in multivariate signals, the proposed solution is the multivariate phase space warping (MPSW) algorithm. First, the multivariate signals are embedded in the reconstructed phase space. Second, the local polynomial receives the current phase space trajectory (PST) to predict the reference PST, after which damage indicators are extracted by comparing the current PST with the reference PST. Third, robust principal component analysis with tensor smooth constraint was proposed on the DIs tensor to extract the main degradation pattern. Finally, the degradation is input to the exponential degradation model to predict the RUL. The run-to-failure experimental datasets for rolling bearings are applied to validate the effectiveness of the proposed MPSW. Experimental results demonstrate that the proposed MPSW effectively tracks the multivariate degradation, and accurately predicts the RUL with distributed sensor networks.
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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 | 0.001 | 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