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Record W2107671701 · doi:10.1109/41.982253

Parametric fault diagnosis for electrohydraulic cylinder drive units

2002· article· en· W2107671701 on OpenAlex

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

VenueIEEE Transactions on Industrial Electronics · 2002
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsActuatorParametric statisticsNonlinear systemHydraulic cylinderControl theory (sociology)Fault (geology)Fault detection and isolationHydraulic machineryProcess (computing)Computer scienceEstimation theoryVolterra seriesControl engineeringEngineeringAlgorithmMathematicsMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A novel model-based methodology for fault diagnosis (FD) of nonlinear hydraulic drive systems is presented in this paper. Due to its linear dependence upon parameters, a second-truncated Volterra nonlinear model is first used to characterize such systems. The versatile order-recursive estimation scheme is employed to determine the values of parameters in the Volterra model. The scheme also avoids separate determination of the model order; thus, the complexity of the search process is reduced. Next, it is shown that the estimated parameters, representing different states of the system, normal as well as faulty conditions, can be used to detect and isolate system faults in a geometric domain. Very promising results are exhibited via simulations as well as laboratory experiments. It is concluded that the developed parametric FD technique has potential to provide efficient condition monitoring and/or preventive maintenance in hydraulic actuator circuits.

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 score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.040
GPT teacher head0.230
Teacher spread0.190 · 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