On hierarchical bayesian based predictive maintenance of autonomous natural gas regulating operations

Leoni, Leonardo; BahooToroody, Ahmad; Abaei, Mohammad Mahdi; Carlo, Filippo De; Paltrinieri, Nicola; Sgarbossa, Fabio

doi:10.1016/j.psep.2020.08.047

Cited by 24 publications

(7 citation statements)

References 50 publications

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“…The activities related to maintenance are a vital point, especially in the last decades, facing two important barriers, one, the high costs that poor management brings with it, and the other, the prolonged downtime. Faced with this problem of insufficient and excessive estimates for the maintenance time interval, Bayesian Networks offer an option for adjusting these time intervals and their associated probabilities [11,70], model multiple maintenance interventions within the life cycle of the physical asset [71], and are also a resilient tool with a high capacity for adaptation [11].…”

Section: Consultation and Analysis Of The Literaturementioning

confidence: 99%

Optimization tools applied to physical asset maintenance management: state of the art

Pentón

Morales

Llanes

et al. 2021

DYNA

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This article presents the state of the art of the application of optimization tools such as Genetic Algorithms, Simulation, Neural Networks, Markov Chains and Bayesian Networks in the physical asset maintenance management. The bibliographic references used were extracted from a detailed search that allowed the selection of the empirical studies presented, in the time horizon from 2010 to 2021, through databases, research platforms and online libraries. The analysis of the identified case studies is carried out, taking into account the variables involved in the study, the optimization tool used, and the result obtained in the analysis of the physical asset maintenance management. The benefits of the application of optimization tools are identified and it is confirmed that maintenance costs and intervention times are present variables, which contribute to the improvement of reliability and maintenance management.

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Section: Consultation and Analysis Of The Literaturementioning

confidence: 99%

Optimization tools applied to physical asset maintenance management: state of the art

Pentón

Morales

Llanes

et al. 2021

DYNA

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“…The use of BN to identify and analyse failure factors for gas pipelines under uncertainty has been reported by various researchers [35][36][37][38][39]. They used the incident databases to identify and investigate loss cases, and together with the pipeline characteristics developed the BN model outlining the failure incident evolution and built a relationship between variables.…”

Section: Literature Reviewmentioning

confidence: 99%

An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks

Hassan

Wang

Kontovas

et al. 2022

Reliability Engineering & System Safety

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“…The first technique has been recently developed by Leoni et al [54] and it consists of four stages as illustrated in Fig. 2.…”

Section: Hierarchical Bayesian Modelling and Crpnmentioning

confidence: 99%

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Leoni

Carlo

Paltrinieri

et al. 2021

Journal of Loss Prevention in the Process Industries

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The integrity of the gas distribution network is crucial to guarantee the safety of human beings and the environment, while avoiding significant financial outlay. Since gas plants are progressively increasing near urban areas, a comprehensive tool to conduct maintenance and reduce the risk arising from the operations is required. To this end risk mitigation strategies have played a pivotal role during the last decades. In this paper, a comparison of three Risk-Based Maintenance (RBM) methodologies able to point out the most critical components, is presented. The first developed technique is a four stages Probabilistic Risk Assessment (PRA), characterized by a Hierarchical Bayesian Network (HBN) to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis (FMECA) to assess the magnitude of the adverse outcomes. The HBN is adopted to overcome the limitations of traditional probability analysis approaches such as Fault Tree (FT), Event Tree (ET) or Bow-Tie (BT). To define a risk metric the total cost of failure is estimated and subsequently the Cost Risk Priority Number (CRPN) is calculated for each equipment. The second approach is a Quantitative Risk Analysis (QRA) carried out via a software named Safeti (by Den Norske Veritas -German Lloyds DNV-GL). By exploiting standard frequencies and modelling the losses of containment through Safeti, the most compelling devices are determined based on their estimated risk integral percentage. At last, Synergi Plant (another software developed by DNV-GL) is adopted for the third methodology. The software provides a Risk-Based Inspection (RBI) plan, through which the components are ranked. The proposed study can provide asset manager a concrete aid to focus maintenance efforts on priority apparatus, while assisting them in adopting the most appropriate methodology to their context. To demonstrate the applicability of the approaches and compare the obtained rankings, a Natural Gas Regulating and Measuring Station (NGRMS) is considered as case of study. The results proofed that all the proposed approaches can be implemented for practical application and the choice of the method strongly depends on the available data.

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On hierarchical bayesian based predictive maintenance of autonomous natural gas regulating operations

Cited by 24 publications

References 50 publications

Optimization tools applied to physical asset maintenance management: state of the art

Optimization tools applied to physical asset maintenance management: state of the art

An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

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