Remaining useful life, technical health, and life extension

Vaidya, Pratichi; Rausand, Marvin

doi:10.1177/1748007810394557

Cited by 26 publications

(43 citation statements)

References 50 publications

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“…The paper suggested Bayesian Belief Network (BBN) as a useful technique for RUL estimation. Vaidya and Rausand (2011) proposed a LE decision making model based on RUL prediction by combining heterogeneous requirements such as degradation modelling, uncertain environmental and operational conditions, uncertain sensor data and expert judgement. The study further concluded that a physics-based approach is the most appropriate technique for supporting LE decision making in the offshore oil and gas industry.…”

Section: Physics-based Approachmentioning

confidence: 99%

“…3) What type of integrity management programme needs to be put into place to support asset operations over the life extension period? In order to provide appropriate answers to above questions, Vaidya and Rausand (2011), Animah et al (2016) and Shafiee et al (2016) suggested, in their respective studies, that it is vital for LE decision makers to estimate the remaining useful life (RUL) of their candidate equipment, as it will enable stakeholders to achieve accurate conclusions during the LE decision-making process. Also, Liao et al (2006) suggested that when ageing degradation is detected, it is important to re-estimate the RUL in order to expedite urgent maintenance decisions and avert possible failures.…”

Section: Introductionmentioning

confidence: 99%

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Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets

Animah

Shafiee

2018

Journal of Loss Prevention in the Process Industries

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Offshore oil and gas assets are highly complex structures comprising of several components, designed to have a lifecycle of 20-25 years of working under harsh operational and environmental conditions. These assets, during their operational lifetime, are subjected to various degradation mechanisms such as corrosion, erosion, wear, creep and fatigue cracks. In order to improve economic viability and increase profitability, many operators are looking at extending the lifespan of their assets beyond the original design life, thereby making life extension (LE) an increasingly critical and highly-discussed topic in the offshore oil and gas industry. In order to manage asset aging and meet the LE requirements, offshore oil and gas operators have adopted various approaches such as following maintenance procedures as advised by the original equipment manufacturer (OEM), or using the experience and expertise of engineers and inspectors. However, performing these activities often provides very limited value addition to operators during the LE period of operation. This paper aims to propose a systematic framework to help operators meet LE requirements while optimizing their cost structure. This framework establishes an integration between three individual life assessment modules, namely: condition assessment, remaining useful life (RUL) prediction and LE decision-making. The benefits of the proposed framework are illustrated through a case study involving a three-phase separator system on a platform which was constructed in the mid-1970s in West Africa. The results of this study affirm the effectiveness of this framework in minimizing catastrophic failures during the LE phase of operations, whilst ensuring compliance to regulatory requirements.

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Section: Physics-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets

Animah

Shafiee

2018

Journal of Loss Prevention in the Process Industries

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“…For a successful implementation of the LE management process, a multi-disciplinary decision-making methodology is required to develop (Vaidya and Rausand, 2011). This is because achieving an efficient LE solution requires inputs from all stakeholders, including designers, system engineers, manufacturers, material specialists, operators and maintenance technicians, health and safety professionals, financial and economic analysts, and human factor researchers.…”

Section: Introductionmentioning

confidence: 99%

“…Hokstad et al (2010) proposed a framework for LE process, integrating material degradation, obsolescence and organisational issues to ensure acceptable technical integrity of offshore assets throughout their life extension period. Vaidya and Rausand (2011) proposed a model for technical health assessment of critical assets for LE and applied it to a subsea raw seawater injection system. Liu et al (2014) presented a framework for managing LE of offshore oil and gas installations in Chaina's Bohai Bay field.…”

Section: Introductionmentioning

confidence: 99%

Development of a techno-economic framework for life extension decision making of safety critical installations

Shafiee

Animah

Simms

2016

Journal of Loss Prevention in the Process Industries

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One of the major decisions in management of the industrial assets is to ensure the feasibility of life extension process for safety critical components when they reach end-of-life. Most of the existing life extension decision-making models are restricted solely to either "technical" or "economic" feasibility analyses that may lead to inaccurate results or incorrect conclusions. In this paper, a comprehensive life extension feasibility assessment framewok by taking into account both the technical and economic considerations is developed. The proposed technoeconomic model for life extension of safety critical elements consists of three phases: preparation, assessment, and implementation. The technical assessment part of the framework incorporates all aspects of data collection and review, screening and prioritization of safety critical elements, condition assessment, estimation of remaining useful life, and risk analysis, while the economic assessment part deals with cost-benefit analysis. The decision to qualify a safety critical element for continuous operation beyond its service life is made based on a "life extension measure (LEM)" which is calculated by combining two indexes of "equipment health condition" and "economic added-value" obtained respectively from the technical and economic assessments. The model is applied to support the life extension decision-making procedure for water deluge systems in offshore oil installations. The results of the study show that the model is highly capable of assisting asset owners to evaluate the technical and economic benefits of extending the service life of components.

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“…An important aspect of prognostics is the accurate estimation of remaining useful life (RUL). Bo Sun et al [8] discuss the benefits of prognostics, and explain how the calculation of RUL is important for technical health determination and life extension [9] in the context of condition-based monitoring [10]. Degradation signals [11], [12] and deterioration models [13] have been used in combination with statistical methods for estimating the remaining useful life in prognostics.…”

mentioning

confidence: 99%

Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods

2014

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In this paper, we investigate the use of first-order reliability methods to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in engineering applications. The prediction of RUL is affected by several sources of uncertainty, and it is important to systematically quantify their combined effect on the RUL prediction in order to aid risk assessment, risk mitigation, and decision-making. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical approaches are computationally cheaper, and sometimes they are better suited for online decision-making. Exact analytical algorithms may not be available for practical engineering applications, but effective approximations can be made using first-order reliability methods. This paper describes three first-order reliability-based methods for RUL uncertainty quantification: first-order second moment method (FOSM), the firstorder reliability method (FORM), and the inverse first-order reliability method (inverse-FORM). The inverse-FORM methodology is particularly useful in the context of online health monitoring, and this method is illustrated using the power system of an unmanned aerial vehicle, where the goal is to predict the end of discharge of a lithium-ion battery.Index Terms-Analytical algorithms, first-order reliability method, ithium-ion battery, model-based prognostics, probability distribution, remaining useful life, uncertainty.

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Remaining useful life, technical health, and life extension

Cited by 26 publications

References 50 publications

Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets

Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets

Development of a techno-economic framework for life extension decision making of safety critical installations

Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods

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