A Multi-Stage Hybrid Open-Circuit Fault Diagnosis Approach for Three-Phase VSI-Fed PMSM Drive Systems
Bibliographic record
Abstract
The performance of Hybrid Electric Vehicles (HEVs), especially in series architecture, is highly dependent on the reliability of electric drive-motor systems. Any failure in power semiconductor devices, such as Insulated-Gate Bipolar Transistors (IGBTs), used in three-phase Voltage-Sourced Inverters (VSIs) for Permanent Magnet Synchronous Motor (PMSM) drive systems, causes a reduction in the reliability and unscheduled maintenance of HEVs. This paper aims at presenting a three-stage combined model-based and data-driven fault diagnosis approach, the so-called hybrid fault diagnosis approach, to detect, locate, and clear open-circuit faults in IGBTs used in VSI-fed PMSM drive systems in HEVs. Field-Oriented Control (FOC), which is a model-based technique, is used to control the electric drive-motor system. The proposed algorithm, which is based on phase voltage analysis, estimates the current in each phase of VSI using the normal operating conditions dataset to detect open-circuit faults in IGBTs. Once a fault is detected, it is located using the faulty conditions dataset and an online data-driven approach, called Modified Multi-Class Support Vector Machines (MMC-SVM) algorithm. Thereafter, the faulty IGBT is bypassed by closing the corresponding backup switch leading to the continuous operation of the electric drive-motor system. The proposed method can accurately and quickly detect, locate, and clear open-circuit faults in IGBTs without the need for additional sensors. Additionally, it shows robustness against back-to-back and simultaneous faults in IGBTs used in VSI-fed PMSM drive systems in HEVs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".