A Low-cost, Minimally-invasive Embedded Monitoring Method for Early Detection of Transistor On-state Resistance Degradation in Power Electronics Systems
Bibliographic record
Abstract
A novel embedded sensing and diagnosis method is proposed for early detection of on-state resistance (RON) variations in power transistors. It is intended for the reliability improvement of power electronics systems, such as DC-DC converters, as is the case in this paper. The method relies on minimally-invasive interfacing circuits, which minimizes the overall reliability degradation, while allowing the operation of the DC-DC converter combined with signal processing algorithms. It is therefore a low-cost solution suitable for embedded applications during the operation of highly-integrated power electronics systems. It allows detecting small on-state resistance (RON) variations in increments of 70 mΩ in switching power transistors, based on the sensing of drain-source voltage (VDS), which is used as a monitoring of component aging or early warning of component failure. The VDS is measured through the combination of a voltage sensing circuit that includes a voltage clamper function to accommodate high VDS voltage, and a read-back function interfacing with a signal processing unit. The read-back signal is processed by a filtering behavioral model which eliminates high-level noise perturbations coming from the supply rail and provides a reliable early awareness of RON variations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".