Angular resampling-sparse representation classification (AR-SRC): A new method for bearing fault diagnosis in non-stationary conditions
Bibliographic record
Abstract
This paper presents a novel methodology for bearing fault diagnosis under non-stationary operating conditions using angular resampling combined with sparse representation classification. The proposed approach addresses variable speed challenges by transforming time-domain vibration signals into the angular domain through encoder-based resampling, enabling extraction of speed-invariant envelope order spectrum features. For classification, a structured dictionary is constructed from training samples for each bearing condition (healthy, inner race, outer race, ball, and combined faults). Sparse coding is performed using the fast iterative shrinkage-thresholding algorithm (FISTA) within an ℓ 1 -norm regularized framework, with final class assignment determined by minimizing reconstruction error across class-specific dictionaries. The proposed framework achieved 99.86% cross-validated accuracy on the Ottawa dataset and demonstrated superiority over state-of-the-art methods (classical machine learning classifiers and some deep learning architecture), all evaluated on identical speed-invariant features. Comprehensive cross-domain validation confirmed robust generalization: cross-speed-profile experiments achieved 99.6% mean accuracy when testing on unseen speed dynamics, while cross-load validation achieved 98.8% mean accuracy across different bearing types and loading conditions. All cross-domain scenarios exceeded 97.8% accuracy. This integrated framework provides a transparent, physically interpretable solution for bearing fault diagnosis, offering enhanced robustness to speed variability and load variations, with practical applicability for industrial condition monitoring under realistic operating conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".