eprintid: 14959
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/49/59
datestamp: 2023-11-10 03:29:33
lastmod: 2023-11-10 03:29:33
status_changed: 2023-11-10 01:58:16
type: article
metadata_visibility: show
creators_name: Mathur, N.
creators_name: Asirvadam, V.S.
creators_name: Aziz, A.A.
title: Mechanical damage assessment for pneumatic control valves based on a statistical reliability model
ispublished: pub
keywords: Damage detection; Pilot plants; Reliability analysis; Safety valves; Weibull distribution, Control operations; Mechanical damages; Prediction-based; Processing plants; Reliability assessments; Simulated process; Statistical modeling; Statistical reliability, Statistical process control
note: cited By 0
abstract: 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2021
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105714263&doi=10.3390%2fs21103307&partnerID=40&md5=f397b67e4e7d82d88d30657ea58ef207
id_number: 10.3390/s21103307
full_text_status: none
publication: Sensors
volume: 21
number: 10
refereed: TRUE
issn: 14248220
citation:   Mathur, N. and Asirvadam, V.S. and Aziz, A.A.  (2021) Mechanical damage assessment for pneumatic control valves based on a statistical reliability model.  Sensors, 21 (10).   ISSN 14248220