Physics-Informed Speed-Integrated Hidden Markov Model for Bearing Fault Diagnosis under Variable Operating Conditions
Bibliographic record
Abstract
Condition monitoring of rolling bearings is essential for ensuring the safe and reliable operation of rotating machinery and for reducing economic losses due to unexpected failures. Under variable-speed conditions, bearing vibration signals exhibit significant nonstationarity and time-varying behavior, posing dual challenges in uncertainty modeling and fault pattern recognition. To address these challenges, this study proposes a speed-integrated explicit duration hidden Markov model (SI-EDHMM), which embeds rotational speed information directly into the probabilistic structure of the model. By constructing a joint observation vector from vibration and speed signals, SI-EDHMM enables accurate characterization of fault features and effective state decoding in nonstationary environments. Fault detection is carried out via a likelihood ratio test. Experimental validation on the Ottawa variable-speed bearing dataset demonstrates that SI-EDHMM significantly outperforms the conventional EDHMM. The detection performance is assessed using the receiver operating characteristic curve and its associated area under the curve (AUC). SI-EDHMM achieves an AUC of 0.9783, representing a 7.12% improvement in detection accuracy over the traditional EDHMM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".