Correcting Domain Shifts in Electric Motor Vibration Data for Unseen Operating Conditions
Bibliographic record
Abstract
This paper addresses the problem of domain shifts in electric motor vibration data created by new operating conditions in testing scenarios, focusing on bearing fault detection and diagnosis (FDD). The proposed method combines the Harmonic Feature Space (HFS) with regression to correct for frequency and energy differentials in steady-state data, enabling accurate FDD on unseen operating conditions within the range of the training conditions. The HFS aligns harmonics across different operating frequencies, while regression compensates for energy variations, preserving the relative magnitude of vibrations critical for fault detection. The proposed approach is evaluated on a detection problem using experimental data from a Belt-Starter Generator (BSG) electric motor, with test conditions having a minimum 1000 RPM and 5 Nm difference from training conditions. Results demonstrate that the method outperforms traditional analysis techniques, achieving high classification accuracy at a 94% detection rate and effectively reducing domain shifts. The approach is computationally efficient, requires only healthy data for training, and is well-suited for real-world applications where the exact application operating conditions cannot be predetermined.
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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".