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Record W7133063997

Multivariate Bayesian Control Chart with Dual Sampling Scheme

2017· dissertation· W7133063997 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2017
Typedissertation
Language
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsControl chartBayesian probabilitySampling (signal processing)Posterior probabilityStatistical process controlControl limitsControl (management)Bayesian inferenceProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Recently, there has been a growing interest among industrial practitioners and researchers for applying economically designed control charts in quality control, condition monitoring, and condition-based maintenance (CBM). Control charts are powerful tools for process control which are used extensively to ensure the process stability over the production run. It has been proved that traditional control charts are not optimal and the control chart parameters should be determined based on the posterior probability that the process is out of control. Design and development of Bayesian control charts in both quality control and CBM applications are the main focuses of this thesis. Traditionally, in designing of a control chart, the observations are collected periodically. However, in many real applications, high sampling cost is associated with collecting observable data, therefore, it could be beneficial to monitor the process/system less frequently when it is in a healthier state and more frequently when a sample shows some indication of a change in the process/system state. The motivation of this thesis comes from such applications when collecting observations is costly, and observations carry partial information about the process or system state. To overcome the drawback of period monitoring, this thesis proposes an optimal control problem in Bayesian framework which uses a novel sampling strategy referred to as dual sampling scheme (DSS) and dual control limits. The system/process is monitored less frequently using a longer sampling interval when the posterior probability is below the warning limit. If the posterior probability exceeds the warning limit, switching to the shorter sampling interval occurs. If the posterior probability exceeds the control limit, the system/process is stopped and full inspection is performed. The proposed model is formulated in both semi-Markov decision process and renewal theory frameworks to obtain the optimal control chart parameters as well as the minimum long-run expected average cost per unit time. For the first time in quality control literature, an explicit formula is derived for computation of average time to signal for multivariate Bayesian control chart with DSS. In addition, the closed-form expressions are derived for system residual life and reliability as functions of the posterior probability statistics.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.116
GPT teacher head0.487
Teacher spread0.372 · 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