Modelling of System Failures in Gas Turbine Engines on Offshore Platforms

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

doi:10.1016/j.ifacol.2015.08.031

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…It can still be said that running overspeed does have an effect on other components of the rotor assembly, potentially exposing component stresses close to their yield strength. Therefore, it is advisable that stress analyses of components should be conducted to determine the status of components following an overspeed event, to determine whether there is an increased probability of failure (Chacartegui, et al, 2011) (HSE, 2006) (Perera, et al, 2015) (Pawsey, et al, 2018) (RMRI Plc., 2009). The failures outlined thus far are all related to the mechanical side of the gas turbine operations.…”

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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“…The NHPP is a process commonly used in reliability. The NHPP are used, for example, in modelling of failures of gas turbine engines [13] failures of hard drives in [14], failures of machine tools in [15], or for modelling software reliability [16].…”

Section: Non-homogeneous Poisson Process and Modellingmentioning

confidence: 99%

Numerical investigation of reliability of pump systems from thermal power plants

Bugatekin

2018

THERM SCI

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This article deals with numerical investigation of reliability of pump systems from thermal power plants. The reliability is examined by using intensity function for a system composed of n-linearly ordered pumps. The time between failures of pumps is taken as experimental. Firstly, appropriate model for the data is determined. Estimates of parameters are obtained with maximum likelihood method and intensity function models are written. Finally, reliability of consecutive 3-out-of-5:F systems are calculated for certain time periods by using the obtained intensity functions.

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“…It is known that relatively little information is contained within safety cases regarding the operation and safety of gas turbines. 1,11,12 What is contained is the turbine model type, manufacture, ISO power rating (in mega watts (MW)), the fuel types and the location of the turbine shown on the respective installation’s drawings. Additional information can be found on occasion, such as text regarding the power generation package or back-up generators.…”

Section: Introductionmentioning

confidence: 99%

“…All of this protection stems from power supplied by the electrical generation systems, which is why offshore platforms and marine vessels ensure that they have back-up generators in the event that one or two generators fail to operate. 12 Usually, on offshore platforms, there are three electrical generation systems, with two in the same module and the third in a separate module on a higher level, which usually acts as the emergency generator. Despite the safety precautions behind the number of generators and their locations, there is still the possibility of all generators failing to operate.…”

Section: Introductionmentioning

confidence: 99%

Utilising Bayesian networks to demonstrate the potential consequences of a fuel gas release from an offshore gas-driven turbine

Loughney

Wang

Matellini

2018

Proceedings of the Institution of Mechanical Engineers, Part M:

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This research proposes the application of Bayesian networks in conducting quantitative risk assessment of the integrity of an offshore gas driven turbine, used for electrical power generation. The focus of the research is centred on the potential release of fuel gas from a turbine and the potential consequences that follow the said release, such as fire, explosion and damage to equipment within the electrical generation module. The Bayesian network demonstrates the interactions of potential initial events and failures, hazards, barriers and consequences involved in a fuel gas release. This model allows for quantitative analysis to demonstrate partial verification of the model. The verification of the model is demonstrated in a series of test cases and through sensitivity analysis. Test case 1 demonstrates the effects of individual and combined control system failures within the fuel gas release model; 2 demonstrates the effects of the 100% probability of a gas release on the Bayesian network model, along with the effect of the gas detection system not functioning; and 3 demonstrates the effects of inserting evidence as a consequence and observing the effects on prior nodes.

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Modelling of System Failures in Gas Turbine Engines on Offshore Platforms

Cited by 7 publications

References 5 publications

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

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

Numerical investigation of reliability of pump systems from thermal power plants

Utilising Bayesian networks to demonstrate the potential consequences of a fuel gas release from an offshore gas-driven turbine

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