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Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model

A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliabili...

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Autores principales: Mathur, Nirbhay, Asirvadam, Vijanth Sagayan, Aziz, Azrina Abd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150588/
https://www.ncbi.nlm.nih.gov/pubmed/34064544
http://dx.doi.org/10.3390/s21103307
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author Mathur, Nirbhay
Asirvadam, Vijanth Sagayan
Aziz, Azrina Abd
author_facet Mathur, Nirbhay
Asirvadam, Vijanth Sagayan
Aziz, Azrina Abd
author_sort Mathur, Nirbhay
collection PubMed
description A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.
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spelling pubmed-81505882021-05-27 Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model Mathur, Nirbhay Asirvadam, Vijanth Sagayan Aziz, Azrina Abd Sensors (Basel) Communication A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure. MDPI 2021-05-11 /pmc/articles/PMC8150588/ /pubmed/34064544 http://dx.doi.org/10.3390/s21103307 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Mathur, Nirbhay
Asirvadam, Vijanth Sagayan
Aziz, Azrina Abd
Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model
title Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model
title_full Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model
title_fullStr Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model
title_full_unstemmed Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model
title_short Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model
title_sort mechanical damage assessment for pneumatic control valves based on a statistical reliability model
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150588/
https://www.ncbi.nlm.nih.gov/pubmed/34064544
http://dx.doi.org/10.3390/s21103307
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