MétaCan
Menu
Back to cohort
Record W2802128272 · doi:10.1139/tcsme-2016-0086

RESEARCH ON CONDITION MONITORING OF SPEED REDUCER OF INDUSTRIAL ROBOT WITH ACOUSTIC EMISSION

2016· article· en· W2802128272 on OpenAlexvenueno aff
Xiaoqin Liu, Xing Wu, Chang Liu, Tao Liu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsReducerRobotIndustrial robotAutomationSIGNAL (programming language)Factory (object-oriented programming)Automotive engineeringEngineeringTransmission (telecommunications)Bearing (navigation)WeldingVibrationComputer scienceMechanical engineeringAcousticsArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

Articulated industrial robots are widely used for factory automation, e.g., the car manufacture industry. As other machines, the transmission system of industrial robot is prone to fail after long term operation. Speed reducer is the key component of the transmission system, and it is important to detect its incipient faults to avoid breaking down. However, until now, few techniques have been proposed to diagnose the reducer without disassembly. Our research on monitoring and diagnosing the speed reducer of industrial robot is reported in this paper. The technique combines inspection method of acoustic emission and signal processing of vibration signal. The characteristics of acoustic emission signal and their connections with mechanical parts of robot reducer have been studied. A defect in the rolling bearing was detected on a welding robot by this technique and confirmed in disassembly.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.269

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 head0.038
GPT teacher head0.269
Teacher spread0.231 · 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 designBench or experimental
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

Citations18
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicFault Detection and Control SystemsFrench-language works237,207