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Research Papers: Materials Technology

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,
Stavanger N-4036, Norway

R. M. Chandima Ratnayake

Department of Mechanical and
Structural Engineering and Materials Science,
University of Stavanger,
Stavanger N-4036, Norway

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received January 17, 2016; final manuscript received November 2, 2016; published online February 17, 2017. Assoc. Editor: Marcelo R. Martins.

J. Offshore Mech. Arct. Eng 139(2), 021401 (Feb 17, 2017) (8 pages) Paper No: OMAE-16-1005; doi: 10.1115/1.4035225 History: Received January 17, 2016; Revised November 02, 2016

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 technology qualification process (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 and 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 for ranking the risk of potential failures in a TQP, especially to reduce the variability present in the risk ranking and the overall uncertainty. This paper 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.

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Figures

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Fig. 1

Novel technology review process (adapted from Refs. [13] and [16])

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Fig. 2

TQ-related main activities (adapted from DNV-OSS-312 [10], DNV-OSS-401 [9], and DNV-RP-A203 [8])

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Fig. 3

An illustrative technology breakdown and levels for subsystems and components of a wind turbine

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Fig. 4

Normalized failure rates of subsystems and assemblies for turbines [34]

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Fig. 5

Normalized hours lost per turbine per year to faults in subsystems and assemblies for turbines [34]

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Fig. 7

CoF (i.e., log10 (delay or down time/hours)) MF

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Fig. 10

Illustrative risk rank calculation

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