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Record W2768478721 · doi:10.1109/taes.2017.2773262

A Thrust Model Aided Fault Diagnosis Method for the Altitude Estimation of a Quadrotor

2017· article· en· W2768478721 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 Aerospace and Electronic Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsAccelerometerFault detection and isolationRedundancy (engineering)BarometerThrustFault (geology)Control theory (sociology)Inertial measurement unitInertial navigation systemComputer scienceEngineeringInertial frame of referenceArtificial intelligenceGeologyAerospace engineeringActuator

Abstract

fetched live from OpenAlex

In this paper, a new fault diagnosis method is presented for the sensors in the vertical direction of a quadrotor. Different from the existing methods that treat the inertial sensors and the measurement sensors separately, the presented method is capable of dealing with both the z-axis accelerometer and the barometer. The knowledge of the thrust model is used to generate an analytical redundancy based fault diagnosis approach for altitude estimation. The filter design, fault detection, isolation, and recovery problems are addressed. An improved chi-test method is used for fault detection. Real-flight data is used to validate the proposed approaches, showing that the faults of the z-axis accelerometer and the barometer can both be detected and the thrust model of a quadrotor can be used to replace the faulty z-axis accelerometer.

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 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 score0.952
Threshold uncertainty score0.405

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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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