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Technical health, remaining useful life and life extension of subsea equipment

Vaidya, Pratichi
Doctoral thesis
Åpne
Fulltext not available (Låst)
Permanent lenke
http://hdl.handle.net/11250/2360471
Utgivelsesdato
2015
Metadata
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Samlinger
  • Institutt for maskinteknikk og produksjon [2648]
Sammendrag
This PhD thesis proposes concepts, tools and processes for analyses of technical health

and remaining useful life (RUL) for decision-making regarding life extension of subsea

equipment. The thesis is the outcome of the Integrated Operations (IO) programme,

hosted at the Norwegian University of Science and Technology, Trondheim. The

mission of the programme is to develop new methods and tools, which can be

embedded in improved work processes in the oil companies and enhanced products

and services from the suppliers. Program 3, subsection 3.1, of the IO, has the mandate

to create a generic framework or a toolbox for remaining useful life prediction.

This work clarifies the meaning of the useful life and the RUL of equipment. It

then discusses the terms useful life and the life termination criteria. It explains the

weaknesses of using the mean remaining life and the ‘technical health index’ for

determination of the RUL. It defines the concept of technical health, presents the

factors that influence the technical health and shows the relation between technical

health and RUL.

This work provides a process to determine a technical condition vector by

bringing in the idea of multi-parameter monitoring. It presents technical health as a

process of assessment of the elements of the technical condition vector, resulting in the

formulation of a technical health picture.

The work clarifies the terms tacit knowledge and explicit knowledge in the

context of technical health. It explains that the technical health is a tacit knowledge and

that all the factors influencing RUL are in tacit form. The knowledge needs to be

converted into explicit form. The work explains the meaning of community of practice

(CoP), and how this can be used to achieve the knowledge conversion.

The work presents various failure modes and mechanisms that are commonly

observed in the subsea industry and recommends suitable modelling approaches based

on several references from top-side (oil and gas), nuclear and avionics industries. It

evaluates the suitability of Bayesian Belief Network (BBN) as a modelling tool for

technical health assessment in the subsea oil and gas industry.

The work explains how to utilize expert judgment, knowledge conversion and

knowledge sharing for technical health assessment and for model selection. A new

approach, combining elements from storytelling and root cause analysis, to address

knowledge conversion and knowledge sharing is presented. A flexible RUL prediction

process is suggested that is adaptable to both objective (classical, frequentist) and subjective (Bayesian) interpretations of probability depending on the availability of

data and suggestions from experts.

Condition based maintenance (CBM) for RUL prediction is not yet a practice

in the subsea oil and gas industry because the real time data acquisition process is not

established. The PhD thesis also describes the work done by various standardization

groups to ensure interoperability among sensors and interfaces i.e., making them smart.

In particular, it informs on how these divergent standards complement each other and

how they can work together to get real time operational data.

The main contributions of the thesis are: (1) Clarification of the terms

technical condition and technical health, (2) Explanation of the relationship between

technical condition, technical health, RUL and decision-making related life extension,

and (3) Presentation of a step-wise process for determination of technical health and

RUL that can be used in the subsea oil and gas industry.
Utgiver
NTNU
Serie
Doctoral thesis at NTNU;2015:136

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