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Record W2969033810 · doi:10.1139/tcsme-2019-0035

Experimental study on wear life of journal bearings in the rotor system subjected to torque

2019· article· en· W2969033810 on OpenAlexvenueno aff
Xinyu Pang, Xuanyi Xue, Xiaowu Jin

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsTorqueRotor (electric)Friction torqueBearing (navigation)LubricationMaterials scienceService lifeTest benchStall torqueControl theory (sociology)EngineeringMechanical engineeringComputer scienceComposite materialDirect torque controlPhysics

Abstract

fetched live from OpenAlex

In the rotor system subjected to torque, the lubrication state of the journal bearings will change, which can lead to wear life change of such bearings. Therefore, the experimental study on the wear life prediction of the journal bearings in the rotor system subjected to torque was carried out. An improved Archard model for wear rate prediction was proposed, which can be applied to determine the relationship among speed, torque, and wear of different bearings. The 20 h wear of a single-span rotor system was tested at three speeds and torque. Test results show that the impact of torque on wear is greater than that of speed. The wear life of a bearing can be predicted with determined wear threshold. The 20 h wear was calculated by using the wear data of the rotor test bench in this model and compared with the actual wear. The comparison results indicate that the accuracy is higher than 92%. The torque – wear life curve shows that wear life is logarithmic to torque at a constant speed and significantly affected by changes in the low torque. Given a constant torque, the wear life will decrease logarithmically with the speed increasing.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 teacher head, 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

Citations6
Published2019
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicTribology and Lubrication EngineeringFrench-language works237,207