Interturn Short-Circuit Fault Detection and Diagnosis in Permanent Magnet Synchronous Motors Using Interactive Multiple Model Strategy
Bibliographic record
Abstract
This paper introduces a novel approach for diagnosing Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) using a two-layer Interactive Multiple Model (IMM) strategy tailored for real-time applications. The first layer employs an IMM-Constrained Extended Kalman Filter (CEKF) framework for fault detection, while the second layer uses an Extended Kalman Filter (EKF) framework for fault diagnosis. In the first layer, four models are employed: the healthy motor model and three models with ITSC faults in each of the PMSM phases. The short-circuit resistance is estimated as an augmented state alongside system states. Upon detecting a fault in one of the phases, the second layer estimates the number of shorted turns and refines the estimated short-circuit resistance value. This study addresses three key challenges in applying the IMM framework to this problem. First, distinguishing between the healthy motor and faulty models with large short-circuit resistances is difficult due to their similar dynamic responses. This issue is resolved by imposing constraints on the estimated resistance, enabling better model separation. Second, the diversity of state variables across models is managed by zero-padding the state vector and estimation error covariance matrix, with matrix-based mode probabilities mitigating errors from augmented zeros. Third, uncertainty in the number of shorted turns is addressed in the second layer by using a secondary model set to determine the exact number of shorted turns and refine the short circuit resistance estimate. Experimental validation, conducted with a custom relay box for controlled fault injection, demonstrates the method's effectiveness in detecting and diagnosing ITSC faults using the first and second model banks, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".