Analysis of Online Partial Discharge Data Trending and Patterns Classification for Life Extension: A Survey of Large Ac Rotating Machines Installed in Petroleum and Chemical Industry
Bibliographic record
Abstract
In the Petroleum and Chemical Industry, a rotating machine has to run reliably in service until a scheduled outage can be planned. In the 1980s and 1990s, some machines operated continuously for 3–5 years, but recently production demands push them to run as long as 5 – 8 years, especially, for some critical applications. These extended run times lead to an asset availability for maintenance once at the end of a 5-8 year run cycle. With opportunities stretched out, it is critical to have a well-defined maintenance scope and execution plan. Online partial discharge (PD) testing and monitoring has been widely applied to determine the need for maintenance of the stator winding insulation system and to help establish a more effective maintenance plan. Several high voltage rotating machines were selected to complete a survey with regards to their online PD trending along with the acquisition of several PRPD patterns. The PRPD patterns obtained contained characteristic features, which were successfully correlated with the observed trend, acquired during the service life of machines. The upward PD trend and correlation of source using phase-resolved PD (PRPD) patterns helped several end-users to determine when maintenance is advisable. During this survey, a few critical units were refurbished based on learning from this survey and then monitored again for an additional 5 years with significant financial savings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".