Development and Characterization of a Non-Intrusive Sensor to Measure Wear in Centrifugal Pumps
Bibliographic record
Abstract
A magnetic sensor is designed and fabricated that allows for impeller blade wear measurement while a centrifugal pump is in operation. The sensor can be installed on existing pumps and does not require structural modification. Over time, as the pump impeller erodes, the gap between the impeller and the pump side plate increases from an unworn width of 0.65 to 2.50 mm, the maximum allowable wear on the testbed pump used for experimental validation. The extent of the impeller wear is determined by measuring over time the change in the reluctance of a magnetic circuit passing through the eroding area. The sensor flux guide mechanism has a relative magnetic permeability of 10000. Flux through the circuit is driven by an inductive coil excited with a 1.0 V AC voltage signal at 70 Hz. As wear occurs, the impeller gap grows and the total reluctance of the magnetic circuit increases, which causes the peak inductance of the coil to drop. Trial data is collected at sampling frequencies up to 500 kHz and then assessed in the frequency domain using fast Fourier transform (FFT). The amplitude of the FFT signal at the pump's rotational frequency is then considered to estimate wear. Sampling data for 1 s at 500 kHz, the sensor demonstrates a maximum signal-to-noise ratio of 17.8 dB with an average sensitivity of 0.022 mV/mm and a resolution of 0.38 mm.
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".