Multivariate Phase Space Warping-Based Degradation Tracking and Remaining Useful Life Prediction of Rolling Bearings
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".