Increasing accuracy of winding insulation state indicator of three phase inverter-fed machines using two current sensors only
Bibliographic record
Abstract
In modern traction drives the application of monitoring systems is growing to ensure continuous operability. Because of the voltage source inverters (VSI) and the high steep voltage change (dv/dt), increased stress of the winding insulation exist. Stator insulation faults are common reasons for a machine breakdown. Insulation health state can be examined by evaluating the transient reaction to a voltage step excitation. Using the inverter as a source of excitation, it is possible to perform an insulation test by evaluating the resulting transient current sensor signals. The trace of the machines transient current reaction depends on the state of the winding insulation system. If insulation degradation occurs the parameters like the parasitic winding capacitances are changing and influencing the trace of the ringing. Normally, for a three phase AC machine the state of every phase is analyzed with the corresponding current sensor. However, regarding the economic issue, the usage of system resources and additional components is restricted. With the proposed method the evaluation of the stator insulation condition is possible only with two current sensors. The state of the phase without a sensor can be analyzed by a special excitation sequence without significant deterioration of sensitivity compared to the results if a sensor is available. Because the transient reaction of non-excited phases is very small, enhanced signal preprocessing is required to prevent sensitivity losses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".