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Record W1586570064 · doi:10.7939/r33t9dm10

Fault Detection of Rotating Machinery from Bicoherence Analysis of Vibration Data

2006· book-chapter· en· W1586570064 on OpenAlex
M.A.A. Shoukat Choudhury, Ming J. Zuo, Sirish L. Shah

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUniversity of Alberta Library · 2006
Typebook-chapter
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBicoherenceSIGNAL (programming language)VibrationSignature (topology)Fault (geology)Fault detection and isolationSignal processingRepresentation (politics)Computer scienceLinearityPattern recognition (psychology)EngineeringArtificial intelligenceControl theory (sociology)AcousticsBispectrumElectronic engineeringMathematicsDigital signal processingPhysicsTelecommunicationsSpectral densityGeology

Abstract

fetched live from OpenAlex

Abstract: The vibration signal carries the signature of faults in most rotating equipments, and early fault detection is possible by analyzing the signal using different signal processing techniques. In this paper we consider a gearbox as a typical representation of a rotating or cyclo-stationary process. Faults in gearboxes leave their signature on the vibration signal and generally manifest themselves as a non-linear transformation in the signal. Bicoherence analysis detects and quantifies the presence of non-linearity in the signal and thus indicates the severity of the fault in the gearbox. In this work, time synchronous averaging is used to find the proper representation of one period of the cyclo-stationary vibration signal. A pilot scale gearbox case study is presented to demonstrate the practicality and utility of the proposed technique.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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