System Failures of Offshore Gas Turbine Engines in Maintenance Perspective

Perera, Lokukaluge P.; Machado, Mario Marcondes; Manguinho, Diego A.P.; Valland, Anders

doi:10.1016/j.ifacol.2016.11.048

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…This is because the generator will operate between 1,500 to 3,000 rpm and the gas turbine can operate anywhere from 3,600 to 20,000 rpm. However, in this research the gas turbine operates at 3,600 rpm (RMRI Plc., 2009) (Perera, et al, 2016) (HSE, 2006). Several events or scenarios may lead to a gas turbine running overspeed.…”

Section: 1mentioning

confidence: 99%

Applying a Bayesian Network Methodology to an Offshore gas Turbine Driven Power Generator to Demonstrate the Cause and Effect Relationship of the Turbine Running Over-speed and the Associated Switchboard Failures

Loughney¹,

Wang²,

Matellini³

2019

Proceedings of the 29th European Safety and Reliability Conference (ESREL)

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This paper investigates the benefits of applying a Bayesian Network in quantitative risk assessment of the integrity of an offshore gas turbine driven generator. The focus of the research is based on the potential failures and incidents associated with an offshore gas turbine running overspeed and failures within the switchboard. The potential consequences that follow said failures, such as fire, explosion and damage to mechanical equipment are also factored into the analysis. A methodology is outlined in order to construct a coherent BN model. This methodology consists of several steps, starting with identifying variables, to then constructing a qualitative BN model from these variables. The methodology culminates in validation of the BN model. A case study, regarding individual and combined component failures is also applied to demonstrate and validate the methodology. The Bayesian network allows the cause-effect relationships to be modelled through clear graphical representation. Similarly, the model can accommodate for continual updating of failure data. Partial validity of the model is demonstrated against some benchmark axioms. It is vital to maintain that the model must remain practical and close to reality from the perspective of gathering data and generating results.

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Section: 1mentioning

confidence: 99%

Applying a Bayesian Network Methodology to an Offshore gas Turbine Driven Power Generator to Demonstrate the Cause and Effect Relationship of the Turbine Running Over-speed and the Associated Switchboard Failures

Loughney¹,

Wang²,

Matellini³

2019

Proceedings of the 29th European Safety and Reliability Conference (ESREL)

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“…Due to the extensive use of this model, research has been developing to improve and develop monitoring based on statistical data models. A successful case study was carried out on turbine engines based on the follow-up condition or monitoring condition and the Analysis of independent Components (ICA) [17], another case was presented in Thailand in the feasibility study of engines for the generation of energy from methane from a landfill using an organic Rankine cycle [18].…”

Section: Introductionmentioning

confidence: 99%

Trends in Failure Studies of Generation Engines based on Statistical Models from 2007 to 2017

Escorcia¹,

Valencia²,

Eras³

2018

JESTR

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The diagnosis of failures in Internal Combustion Engines (ICM)-generation engines, has been considering one of the principal thematic axes of academic and industrial Research, Development and Innovation (R&D&I) based on the analysis of the specialized engineering literature within the Distributed Generation (DG) paradigm. With the aim of presenting the characteristics of the work related to failures in ICM and recognizing the global research focuses, a scientometric methodological approach of a systematic review of the publications indexed in Web of Science (WoS) has been carrying out from the perspective of bibliometric analysis for the period from 2007 to 2017. The H-index has been incorporating into this analysis to assess the visibility and impact of journals, authors, countries, and institutions with the highest levels of production and recognition in the field under study. The systematic review also made it possible to analyze the interaction between knowledge groups and networks with the authors and institutions identified in the ranking. The results show a significant increase in the number of publications, especially between 2012 and 2016, which allow the main dimensions of R&D&I related to the study of failures in ICM to be characterized and illustrated at a holistic level and provide added value to researchers interested in establishing cooperation and publication processes with journals, authors, institutions and potential actors in the study of ICM failures.

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