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Record W2765099665 · doi:10.1016/j.ifacol.2017.08.2586

Comparison of Two Basic Statistics for Fault Detection and Process Monitoring

2017· article· en· W2765099665 on OpenAlexaff
Zhiwen Chen, Kai Zhang, Yuri A.W. Shardt, Steven X. Ding, Xu Yang, Chunhua Yang, Tao Peng

Bibliographic record

VenueIFAC-PapersOnLine · 2017
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStatisticStatisticsFault detection and isolationConstant false alarm rateFalse alarmFault (geology)Scan statisticComputer scienceMathematicsData miningPattern recognition (psychology)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, two common statistics, the T2 and the Q statistics, for fault detection and process monitoring are compared. Specifically, the geometric relationship between the T2 statistic and 3 common forms of the Q statistics is analysed. Furthermore, using the false alarm rate (FAR) and the fault detection rate (FDR), the fault detection performance of both statistics is quantified and compared. The results show that, for a given significance level, the T2 statistic has the best overall FDR.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.122
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.339
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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