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Simulation-based Vibration Sensor Placement for Centrifugal Pump Impeller Fault Detection

2020· article· en· W3041526731 on OpenAlexaff
Alireza Zabihihesari, Farzad A. Shirazi, Alireza Riasi, M. J. Mahjoob, Erfan Asnaashari

Bibliographic record

VenueApplied and Computational Mechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsYork University
Fundersnot available
KeywordsCasingVoluteImpellerCentrifugal pumpVibrationAccelerometerFault (geology)AccelerationAcousticsActuatorFault detection and isolationSpecific speedMaterials scienceEngineeringStructural engineeringMechanicsMechanical engineeringComputer scienceGeologyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, a simulation-based method is proposed for optimal placement of vibration sensors for the purpose of fault detection in a centrifugal pump. The centrifugal pump is modeled to investigate the effect of vane tip fault on fluid flow patterns numerically. Pressure pulsations are investigated at different locations at the inner surface of the pump before and after the presence of the fault to determine the best location for installing vibration sensors on the pump casing. Experiments are also conducted by mounting accelerometers at various locations on the pump casing. Simulation and experimental results are then compared and a direct correlation between changes in PSD amplitudes of pressure and acceleration signals was observed. The optimum location for placement of an accelerometer is determined to be near the volute tongue on the casing where the highest level of pressure pulsations in the simulation is also calculated in the presence of vane tip fault.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.214
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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