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research-article

Minimization of risk assessments' variability in technology qualification processes

[+] Author and Article Information
S.M. Samindi M.K. Samarakoon

Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, N-4036, Stavanger, Norway
samindi.samarakoon@uis.no

R.M. Chandima Ratnayake

Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, N-4036, Stavanger, Norway
chandima.ratnayake@uis.no

1Corresponding author.

ASME doi:10.1115/1.4035225 History: Received January 17, 2016; Revised November 02, 2016

Abstract

Technology qualification (TQ) centers on establishing an acceptable level of confidence in innovative aspects of new technologies that are not addressed by the normative standards and/or common certification procedures. Risk-based technology qualification aims to minimize the uncertainty and risk of potential failures in novel designs, concepts or applications that are not covered by existing standards, industry codes and/or best practices. The degree of success in a TQP depends on its potential for minimizing the uncertainty of a novel technology under assessment and the level of uncertainty arising from the qualification methods and basis. Due to the lack of generic reliability data, focused research & development and in-service experience, it is necessary to employ risk-based qualification of new technology. In a risk-based TQ, the technology under consideration is decomposed into manageable elements to assess those that involve aspects of new technology and to identify the key challenges and uncertainties. The aforementioned requires risk ranking with the support of experts, who represent relevant technical disciplines and field experience in: design, fabrication, installation, inspection, maintenance and operation. Hence, it is vital to have a comprehensive approach to ranking the risk of potential failures in a TQP, especially to reduce the variability present in the risk ranking and the overall uncertainty. This manuscript proposes a fuzzy logic based approach, which enables the variability present in the risk ranking of a TQP to be minimized. It also demonstrates how to make risk rankings by means of an illustrative case.

Copyright (c) 2016 by ASME
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