Improving the reliability of automation systems using data model analysis

2021 ◽  
Author(s):  
A. I. Baranchikov ◽  
I. I. Yakovlev ◽  
Yu. V. Redkin
Keyword(s):  
Data Model ◽  
Model Analysis ◽  
Automation Systems ◽  
Using Data

scholarly journals Distance-Based Estimation Methods for Models for Discrete and Mixed-Scale Data

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 107
Author(s):  
Elisavet M. Sofikitou ◽  
Ray Liu ◽  
Huipei Wang ◽  
Marianthi Markatou
Keyword(s):  
Data Model ◽  
Null Hypothesis ◽  
Model Misspecification ◽  
Asymptotic Properties ◽  
Estimation Methods ◽  
Measurement Scale ◽  
Interval Scale ◽  
Pearson Residuals ◽  
Using Data ◽  
Scale Data

Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.

scholarly journals Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

2021 ◽  
Author(s):  
Sarah Bauermeister ◽  
Joshua R Bauermeister ◽  
R Bridgman ◽  
C Felici ◽  
M Newbury ◽  
...  
Keyword(s):  
Data Model ◽  
Data Access ◽  
Data Discovery ◽  
Standard Data ◽  
Technology Standard ◽  
Access Request ◽  
Nested Structure ◽  
Using Data ◽  
Model C ◽  
Cohort Analyses

Abstract Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

Building a Low-Manned Production Installation: Considerations for Rotating Equipment, Electrical & Automation Systems, and Digitalization

2021 ◽  
Author(s):  
Elgonda LaGrange
Keyword(s):  
Oil And Gas ◽  
Cost Savings ◽  
Decision Makers ◽  
The North ◽  
Automation Systems ◽  
The North Sea ◽  
Rotating Equipment ◽  
Using Data ◽  
Areas Of Interest ◽  
Insight Into

Abstract Nearly all oil and gas operators and engineering companies in the offshore sector today are engaged in programs to advance concepts for low-manned and/or normally unattended production installations (NUIs). When it comes to the design of these facilities, topsides rotating equipment and electrical, instrumentation, control, and telecommunications (EICT) packages represent key areas of interest for decision-makers, owing to the significant impact they can have on required manning levels. Over the past decade, the author's company has worked closely with major Operators in the U.S. and the North Sea to look at how existing technologies can be applied in these areas to safely facilitate de-manning of both brownfields and greenfields. This paper provides insight into these efforts. It also presents projected manpower and cost savings from de-manning, using data derived from both studies and real-world projects.

Autonomous journaling response using data model LUTS

2009 ◽  
Author(s):  
Holger Jaenisch ◽  
James Handley ◽  
Nathaniel Albritton ◽  
David Whitener ◽  
Randel Burnett ◽  
...  
Keyword(s):  
Data Model ◽  
Using Data
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