Experimental Study: Bearing Degradation Caused by Electrical Currents and Voltages at Low Speeds
Bibliographic record
Abstract
In electric vehicles (EVs), bearings in traction motors are increasingly prone to electrical damage under operational currents and voltages, leading to accelerated wear and reduced lifespan. This study examines the extent of bearing damage under low-speed, electrically charged conditions to understand wear behavior at boundary lubrication better. Bearings were driven at low speed by a motor, with inner and outer rings connected to a pulsed power supply’s positive and negative terminals, simulating real-world shaft voltage conditions. The applied electrical parameters included voltages from 5 V to 240 V and frequencies of 10 kHz, leading to voltages at the bearing peaking between 0.1 and 12 V measured by an oscilloscope and multimeter. The tested bearings were disassembled, and scanning electron microscopy (SEM) was used to assess the damage associated with varying electrical stresses. The results revealed distinct wear patterns and degradation effects when the shaft current and peak voltage reached 2.5 A and 12 V, emphasizing the critical need for protective strategies. Future work will focus on evaluating the impact of higher rotational speeds and controlled power supply conditions to analyze the effects of increased power supply settings and compare outcomes to low-speed scenarios.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".