Physics-Informed Speed-Integrated Hidden Markov Model for Bearing Fault Diagnosis under Variable Operating Conditions
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
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it