How safe is the insulation system of rotating machines operating in gas groups B, C & D?
Bibliographic record
Abstract
The safe operation of electrical rotating machines in Chemical, Oil and Gas industrial environments where hazardous gas may be present is of primary concern. There are numerous publications available on the subject of arc and spark at various locations within rotating electric machines. Various techniques have been used to minimize corona discharge activity in high voltage stator windings. To understand the impact of discharge activity in a hazardous environment, few manufacturers have tested or provided such data. To ensure safe operation of machines in these environments, it is necessary to determine and understand the levels of partial discharge (PD) and corona discharge activities. PD and corona discharge activities are phenomena related to the applied voltage. There are many other design and environmental related factors that can cause variation in the level of discharge activity. It is believed that if the PD or corona discharge activity exceeds certain limits, it may ignite a specific explosive gas or vapor of gas as defined in the IEC Std. 60079-15. To assess the safe operation of insulation systems of 6.6 kV to 13.8 kV, the steady state ignition tests, called Incendivity testing was performed on stator windings in gas groups B, C, and D as specified in IEC Std. 60079-15. The contributing factors and mitigation of discharge activity will also be discussed in this paper.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".