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Record W2144629579 · doi:10.1109/cdc.2009.5400042

Sensor and actuator fault detection and isolation for a high performance aircraft engine bleed air temperature control system

2009· article· en· W2144629579 on OpenAlexaff
Lan Shang, Guangjun Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFault detection and isolationActuatorKalman filterControl theory (sociology)Fault (geology)EngineeringExtended Kalman filterControl systemControl engineeringComputer scienceControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, an unscented Kalman filter (UKF) based method is developed to detect and isolate both sensor and actuator faults and applied to a high performance aircraft bleed air temperature control system. Known UKF based fault detection and isolation methods deal with sensor faults or actuator faults only. For the aircraft bleed air temperature control system studied in this paper, potential faults associated with both the temperature sensor and valve actuators of the control system need to be considered. The proposed method contains two unscented Kalman filters: one is used to detect sensor fault and the other is dedicated to actuator fault. Nonlinear state space equations describing the engine bleed air temperature control system dynamics are derived and utilized in the design and analysis of the proposed fault detection and isolation method. Computer simulations and experiments have been conducted, and the proposed fault detection method is shown to be effective in detecting and isolating sensor and actuator faults.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.629

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 head0.002
GPT teacher head0.170
Teacher spread0.167 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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