Fault Detection and Identification Scheme for Dual-Inverter Fed OEWIM Drive
Bibliographic record
Abstract
Dual-inverter configuration has been attracting increasing attention, especially in motor drives, for its advantages in fault tolerance, multilevel modulation effect, dc voltage utilization, etc. However, its switch fault diagnosis is difficult due to the symmetry of the two sub-inverters. A signal processing approach is proposed in this paper to detect the switch open-circuit fault occurrence and further identify the specific faulty switch, benefiting maintenance and fault-tolerant operation. In this scheme, the faulty phase and the faulty switch pair, consisting of the faulty switch and the switch on its diagonal, are identified using the stator phase currents average values. Following that, the bridge of the faulty phase is forced into a unipolar mode by artificially turning off one switch in the healthy pair continuously. Through properly controlling the duty ratios of the other switches in this bridge, the faulty switch is allowed to be further identified from the faulty pair by analyzing the behavior of the induced current in this phase. This approach is independent of either the speed or the voltage information. The effectiveness is validated through experiments on a sensorless controlled open-end winding induction motor drive.
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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".