Effect of uncertainty of reliability data of instrumentation on risk assessment and prediction in process plant applications
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University of L'Aquila
Submission date: 2018-07-31
Final revision date: 2018-10-08
Acceptance date: 2018-11-29
Online publication date: 2018-12-17
Publication date: 2018-12-17
Corresponding author
Emanuela Natale   

University of L'Aquila, via Gronchi, 67100 L'Aquila, Italy
Diagnostyka 2019;20(1):73-79
Some aspects are discussed concerning the uncertainty causes in risk assessment techniques of industrial interest. Particular attention has been paid to the evaluation of the effect of uncertainty of reliability data of devices and instruments to be used in process plants on the evaluation of the occurrence frequency of the top event. The influence of measuring equipment, whose contribution to the whole uncertainty appears in some cases very important, has been analysed with reference to both operative and environmental aspects.
Marszal EM. Tolerable risk guidelines. ISA Trans. 2001; 40(4): 391-399. https://doi.org/10.1016/S0019-....
BS OHSAS 18001:2007, Occupational Healt and Safety management systems – Specification.
DIRECTIVE 2012/18/EU of the European Parliament and of the Council of 4 July 2012 on the control of major-accident hazards involving dangerous substances, amending and subsequently repealing Council Directive 96/82/EC.
D’Aponte F, D’Emilia G, Lupinetti S, Natale E, Pasqualoni P. Uncertainty of slip measurements in a cutting system of converting machinery for diapers production. Int. J. Metrol. Qual. Eng. 2015; 6(3): 1-6. https://doi.org/10.1051/ijmqe/....
Lees FP. Loss prevention in the process industries. London: Butterworth & Co; 2012.
AICHE: Guidelines for chemical Process Quantitative Risk analysis. 2nd Ed., Center for chemical Process Safety. 2000.
Skelton B. Process safety analysis. Houston: Gulf Publishing Co. 1997.
Henley EJ, Kumamoto H. Reliability engineering and risk assessment. Englewood Cliffs; Prentice-Hall, Inc. 1981.
Ruijters E, Stoelinga M. Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review 2015; 15-16: 29-62. https://doi.org/10.1016/j.cosr....
Szkoda M, Kaczor G. Reliability and availability assessment of diesel locomotive using fault tree analysis. Archives of transport 2016; 40(4): 65-75. https://doi.org/10.5604/086695....
Aldemir T. A survey of dynamic methodologies for probabilistic safety assessment of nuclear power plants. Ann. Nucl. Energy 2013; 52: 113-124. https://doi.org/10.1016/j.anuc....
Jin H, Lundteigen MA, Rausand M. Reliability performance of safety instrumented systems: A common approach for both low- and high- demand mode of operation. Reliab. Eng. Syst. Safe. 2011; 96 (3): 365-373. https://doi.org/10.1016/j.ress....
Muhammad, Hidekazu Y, Takeshi M, Ming Y. Common cause failure analysis of PWR containment spray system by GO-FLOW methodology. Nucl. Eng. Des. 2013; 262: 350-357. https://doi.org/10.1016/j.nuce....
Durga Rao K, Gopika V, Sanyasi Rao VVS, Kushwaha HS, Verma AK, Srividya A. Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliab. Eng. Syst. Safe. 2009; 94(4): 872-883. https://doi.org/10.1016/j.ress....
Poprocký R, Galliková J, Stuchlý V, Volna P. FMEA analysis of combustion engine and assignment occurrence index for risk valuation, Diagnostyka 2017; 18(3): 99-105.
D’Emilia G, Di Rosso G, Gaspari A, Massimo A. Metrological interpretation of a six sigma action for improving on line optical measurement of turbocharger dimensions in the automotive industry, P I Mech Eng D-J Aut 2015; 229(2): 261-269. https://doi.org/10.1177/095440....
Nelson W. How to analyse reliability data. Milwaukee: ASQC Quality Press; 1983.
Moura EC. How to determine sample size and estimate failure rate in life testing. Milwaukee: ASQC Quality Press; 1991.
ISO 9001:2015 Quality management systems – Requirements.
ISO 14001:2015, Environmental management systems - Requirements with guidance for use.
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