Alternative Methods of Machine Online Condition Monitoring; Recommendations for Rotating Machines in Petroleum and Chemical Industry
Bibliographic record
Abstract
Rotating machines health assessment is an important aspect of machine operation. There are several online tests and techniques available to assess machine condition during service. Online condition monitoring and assessment helps to detect changes in machine operating condition at an early stage as well as determining the degree of degradation over time. Assessing machine condition can help estimate the risk of failure and the potential vulnerability to unexpected or unintended operating events. The objective is to promptly initiate corrective measures preventing costly shutdowns and production losses. The online monitoring is also helpful in being well prepared ahead of scheduled shutdown. In this paper, individual online tests and monitoring techniques are discussed on both synchronous and induction machines rated between 4.0 kV and up to 13.8 kV. The online monitoring techniques covered are; rotor flux monitoring, stator endwinding vibration monitoring, broken rotor bar detection, stator inter-turn fault detection and online partial discharge monitoring. A systematic approach based on years of experience and field studies is presented to provide a clear path for selecting appropriate tests and to choose an optimized and best possible online monitoring techniques that can be used in the Petroleum and Chemical Industry.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".