Dynamic Reliability Prediction of Asset Failure Modes

Gallego‐Fernández, Juan B.; Bermejo, Jesús Ferrero; Polo, Fernando Agustín Olivencia; Márquez, Adolfo Crespo; García, Gonzalo Cerruela

doi:10.1007/978-3-319-58045-6_12

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…The main advantage of dynamic reliability is the possibility to address the evaluation of a system both in terms of dependability attributes (reliability, availability and maintenance) and performance (production and other relevant key performance indicators, like the service availability). Several contributions in industrial [37] and nuclear applications have already shown the improved accuracy of this modelling paradigm [14,38], supported also by other works [39][40][41][42][43] addressing the evaluation of the failure rates with respect to the system working conditions. Unfortunately, the failure behavior of a system component with respect to the system operating conditions is not always known [44,45] and this represents the most important limitation for the use of dynamic reliability approaches.…”

Section: Related Workmentioning

confidence: 61%

“…Current hesitancy in the use of dynamic reliability models is mainly caused by the unavailability of exact models of which account for all the possible variations of the working conditions γ(L,X,S) [44,45]. Recently, with the advance of condition-based monitoring techniques, reliability estimations are being improved with up-to-date degradation and operation information [39][40][41][42][43]. In the definition of a fault tree, the SHyFTA model supports both traditional and hybrid basic events.…”

Section: Stochastic Hybrid Fault Tree Automaton (Shyfta)mentioning

confidence: 99%

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On the use of dynamic reliability for an accurate modelling of renewable power plants

Chiacchio

D’Urso

Famoso

et al. 2018

Energy

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Renewable energies are a key element of the modern sustainable development. They play a key role in contributing to the reduction of the impact of fossil sources and to the energy supply in remote areas where the electrical grid cannot be reached. Due to the intermittent nature of the primary renewable resource, the feasibility assessment, the performance evaluation and the lifecycle management of a renewable power plant are very complex activities. In order to achieve a more accurate system modelling, improve the productivity prediction and better plan the lifecycle management activities, the modelling of a renewable plant may consider not only the physical process of energy transformation, but also the stochastic variability of the primary resource and the degradation mechanisms that affect the aging of the plant components resulting, eventually, in the failure of the system. This paper presents a modelling approach which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton. The main steps for the design of a renewable power plant are discussed and implemented to estimate the energy production of a real photovoltaic power plant by means of a Monte Carlo simulation process. The proposed approach, modelling the failure behavior of the system, helps also with the evaluation of other key performance indicators like the power plant and the service availability.

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Section: Related Workmentioning

confidence: 61%

Section: Stochastic Hybrid Fault Tree Automaton (Shyfta)mentioning

confidence: 99%

On the use of dynamic reliability for an accurate modelling of renewable power plants

Chiacchio

D’Urso

Famoso

et al. 2018

Energy

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“…It makes no assumptions about the probability distribution, which is used to estimate equipment reliability . The Kaplan‐Meier estimator for asset reliability assessment can be calculated with few assumptions required . The Kaplan‐Meier estimator is also appropriate in the cases where the failure time data or some failure data are unknown …”

Section: Methodsmentioning

confidence: 99%

“…42 The Kaplan-Meier estimator for asset reliability assessment can be calculated with few assumptions required. 43 The Kaplan-Meier estimator is also appropriate in the cases where the failure time data or some failure data are unknown. 6,13 Assume the failure observation times are t 1 < t 2 < … < t k , where k denotes the number of observations.…”

Section: Kaplan-meier Estimatormentioning

confidence: 99%

Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant

Jolfaei

Jin

Linden

et al. 2019

Quality & Reliability Eng

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Power generators are critical assets in wastewater treatment plants (WWTPs) in Australia and many countries. Better managing the lifetime, minimising failures, improving reliability and availability, and reducing operating and maintenance costs of the power generation assets are still challenging topics for water utilities. This case study aims to develop power generation system reliability and availability modelling considering redundancy to minimise operation and maintenance costs. The two‐parameter Weibull model was used to assess system reliability and availability to power generation engines in WWTPs. The Kaplan‐Meier method (a time‐driven estimation technique) and the log beta‐Weibull model (which is suitable for modelling censored and uncensored data) were used to analyse and validate the modelling results. Shape and scale parameters of the Weibull models were estimated by maximising the log‐likelihood function using non‐linear optimisation. Hazard and reliability functions were calculated using the Weibull model. Results using two‐parameter Weibull, Kaplan‐Meier, and log beta‐Weibull models display low reliability and high hazard rate over time, which was associated with spark plug failure due to a suboptimal start and stop operation strategy.

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