Ageing PSA incorporating effectiveness of maintenance and testing

Martón, Isabel; Sánchez, Ana Isabel; Martorell, Sebastián

doi:10.1016/j.ress.2015.03.022

Cited by 30 publications

(37 citation statements)

References 11 publications

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“…Risk assessment methodologies have widely been used for this purpose in some industries (e.g., see Palkar and Markeset, 2012;Liu et al, 2014;Carvalho et al, 2015). On the other hand, probabilistic safety assessment (PSA) models such as analytical unavailability and unreliability models have also been employed in the nuclear industry to determine current health status of safety related systems (e.g., see Martorell et al, 1999;Kancev et al, 2011;Kancev and Cepin, 2012;Martón et al 2015). Prognostic Health Management (PHM) is also gaining prominence in condition assessment and predicting remaining useful life (RUL) of safety related systems.…”

Section: Technical Assessment Modulementioning

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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Section: Technical Assessment Modulementioning

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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“…a) Unreliability contributions Adopting the basis of the formulation of unreliability contributions in Ref. [6] considering the contributions introduced in sections 2.1.1 and 2.1.2, the item unavailability due to unreliability contributions can be evaluated using the following equations :…”

Section: Unavailability Modelsmentioning

confidence: 99%

“…For example, analytical age-dependent unavailability models have been developed adopting linear ageing rates [1], which considers the impact of testing and maintenance activities at least implicitly [2][3][4][5]. Nowadays, one can find several proposals in the literature to integrate such a kind of RAM modelling into the so called Ageing PSA (APSA) [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Ref. [6] demonstrates the importance of addressing explicitly the effectiveness of maintenance in managing equipment ageing and test efficiency in detecting hidden failures as this may impact the accurate planning and optimization of testing and maintenance activities based on RAM criteria. This is even more important when the component consists of multiple items, where every item may undertake specific maintenance and testing activities to cope with different degradation mechanisms and failure causes respectively affecting the multiitems component.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness

Martón

Martorell

Mullor

et al. 2016

Reliability Engineering & System Safety

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There are many models in the literature that have been proposed in the last decades aimed at assessing the reliability, availability and maintainability (RAM) of safety equipment, many of them with a focus on their use to assess the risk level of a technological system or to search for appropriate design and/or surveillance and maintenance policies in order to assure that an optimum level of RAM of safety systems is kept during all the plant operational life. This paper proposes a new approach for RAM modelling that accounts for equipment ageing and maintenance and testing effectiveness of equipment consisting of multiple items in an integrated manner. This model is then used to perform the simultaneous optimization of testing and maintenance for ageing equipment consisting of multiple items. An example of application is provided, which considers a simplified High Pressure Injection System (HPIS) of a typical Power Water Reactor (PWR). Basically, this system consists of motor driven pumps (MDP) and motor operated valves (MOV), where both types of components consists of two items each. These components present different failure and cause modes and behaviours, and they also undertake complex test and maintenance activities depending on the item involved. The results of the example of application demonstrate that the optimization algorithm provide the best solutions when the optimization problem is formulated and solved considering full flexibility in the implementation of testing and maintenance activities taking part of such an integrated RAM model.

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“…The difference between ALMs and PHMs lies in the modelling of the dependence of the aging process on the covariates. While in PHMs the effects of covariates are modelled as multiplicative factors in the failure rates, ALMs model explicitly the operating environment impacts on TTFs [5,[7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Naseri

Baraldi²,

Compare³

et al. 2016

Reliability Engineering & System Safety

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We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions. Keywords: Dynamic weather conditions, failure rate, repair rate, equivalent age, preventive maintenance, availability, Monte Carlo simulation, oil and gas, Arctic offshore. , ′ ); = 1, … , ; 0 ′ = 0, partitioning the time horizon, during which the weather conditions remain unchanged at an intensity level of . In this study, the time interval length is taken to be equal to a day. Acronyms ALMReliability of a component operating under static weather conditions at WIL State of the system at WIL , = 0, … , ; = 1 and = 0 refer to the functioning and faulty states, respectively. Waiting downtime before commencing CM tasks in the base area, which includes the time required to shut down the unit, issue the work orders, wait for the spare parts, and start up the unit after repair.Weather element referring to either minimum daily air temperature or maximum daily wind speed, i.e., ∈ { , ′ } ( ) Maximum wind speed during in km/hr ′ ( ) Box-Cox transformed wind speed at the th day The factor by which the weather-dependent factor , changes due to modifications to plant design The factor by which the weather-dependent factor , changes due to modifications to the comfort of maintenance crew, or modifications to the plant design resulting in changes in component active repair time 0 Weibull shape parameter, estimated using the life data collected in the base area (i.e., normal weather conditions), partitioning the time horizon; during each time interval the weather conditions are assumed constant Weather-dependent multiplicative factor corresponding to the WIL , = 0, … , with 0 = 1, which accounts for the reductions in TTFs. Weather-dependent multiplicative factor corresponding to the WIL = , = 0, … , at the th time interval = 1, … , which accounts for the reductions in TTFs Weather-dependent multiplicative factor corresponding to the WIL , = 0, … , at the th time interval = 1, … , which accounts for the rises in TTRs Weather-dependent multiplicative factor corresponding to the WIL = , = 0, … , at the th time interval = 1, … , which accounts for the rises in TTRs * Modified weather-dependent multiplicative factor corresponding...

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Ageing PSA incorporating effectiveness of maintenance and testing

Cited by 30 publications

References 11 publications

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

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

Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

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