Bibliographic record
Abstract
Fault tolerance in electric motor drives has been extensively studied due to their widespread usage in safetycritical applications, including more/all-electric aircraft and electric vehicles. SRMs are renowned for their high robustness and excellent fault tolerance. However, they are not immune to faults, and a potential fault in any part of the motor drive could have serious implications for the system, even may lead to unexpected shutdown if the fault is not promptly remedied. The power converter is a key component and the most vulnerable to failure, as reported for 35% of the drive faults. Fault tolerance has typically been achieved through a combination of hardware and software reconfigurations. However, due to the simple structure of the standard asymmetrical half-bridge (AHB) converter, hardware reconfiguration is more commonly used to remediate converter faults. Consequently, fault tolerance adds cost and complexity to a standard SRM drive system. This paper presents an instructive survey of the recent research on fault tolerant SRM drives. Moreover, the post-fault performance, implementation costs, and fault-tolerant capabilities of advanced fault-tolerant converters are systematically quantified, evaluated and compared. Finally, the paper is concluded with an investigation on the future research trends in fault tolerance in SRM drives.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".