University of Ottawa constant and variable speed electric motor vibration and acoustic fault signature dataset
Bibliographic record
Abstract
Induction motors are used in industry as they are self-starting, reliable, and affordable. Applications for these motors include lathes, mills, pumps, power conveyor belts, and commercial electrical and hybrid vehicles. Induction motors have various types of failures, including rotor unbalance, rotor misalignment, stator winding faults, voltage unbalance, bowed rotor, broken rotor bars, and faulty bearings. There is a need for differentiating mechanical faults from electrical fault signals when identifying what part of the motor needs maintenance while using machine learning. Therefore, data collection is essential for electric motor fault diagnosis. The University of Ottawa Electric Motor Dataset - Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) is provided to address this issue. Data from accelerometers, temperature, and acoustic sensors are collected to provide quality electric motor fault data. The dataset includes various induction motor faults useful for time domain analysis. The high-quality data provided by this dataset will help facilitate the differentiation between mechanical faults and electric faults when using fault detection methods, which is a valuable asset for machine condition monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".