Monitoring the Best Operating Point of Centrifugal Pumps Using Blade Passing Vibration Signals
Bibliographic record
Abstract
Degradation of centrifugal pumps can occur over time and can often lead the pump to operate away from best efficiency point.Therefore, it is necessary to utilize monitoring techniques to ensure the proper operation.In this work, the effect of operating the pump away from the best efficiency point on the vibration characteristics is investigated.An industrial centrifugal pump rated at 7.5 kW is installed in a flow testing loop to determine the pump performance and the vibration response at the full operational range the pump.The vibration response was measured using accelerometers placed on the suction and discharge flanges of the pump casing.The amplitude of the blade passing frequency represented by FFT spectrum is determined for various flow rates.The minimum amplitude of the blade passing frequency was found to occur at a flow rate of 17 L/s.Meanwhile, the entire performance of the pump was evaluated and found that the best efficiency of 72.5 % occurs at 17 L/s.Furthermore, the pump efficiency was found to decrease as the amplitude of the blade passing frequency increases.The results show that the amplitude of the blade passing frequency can be used to help monitor the pumps best operating point.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".