Development of a tool to detect faults in induction motors via current signature analysis
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
This paper demonstrates through industrial case histories, how current signature analysis can reliably diagnose rotor cage problems in induction motor drives. Traditional CSA measurements can result in false alarms and/or misdiagnosis of healthy machines due to the presence of current frequency components in the stator current resulting from nonrotor related conditions such as mechanical load fluctuations, gearboxes, etc. Theoretical advancements have now made it possible to predict many of these components, thus making CSA testing much more robust and less error prone technology. Based on these theoretical developments, case histories are presented which demonstrate the ability to separate current components resulting from mechanical gearboxes from those resulting from broken rotor bars. From this data, a new handheld instrument for reliable detection of broken rotor bars, air gap eccentricity, shorted turns in LV stator windings and mechanical phenomena/problems in induction motor drives is being developed and is described. Detection of the inception of these problems prior to failure facilitates remedial action to be carried out thus avoiding the significant costs associated with unexpected down time due to unexpected failures.
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.
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 | 0.001 | 0.001 |
| 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 it