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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".