Abstract
Abstract System reliability of an offshore power plant with several gas turbine engines is analyzed in this study to understand the failure intensity of a selected gas turbine engines under varying maintenance activities. A set of event data of a selected gas turbine engine is considered to identify system failure intensity levels, where unknown maintenance actions were implemented (i.e. various repairs disturb the failure rate). A non-homogeneous Poisson process (NHPP) is used to model the age dependent failure intensity of the same gas turbine engine and the maximum likelihood estimation (MLE) approach for calculating the respective model parameters is proposed. Several failure intensity rates (i.e. varying failure trends) in these models (i.e. during the system age of the gas turbine engine) are observed. Furthermore, these varying failure trend models are evaluated with actual failure events of the same gas turbine engine by considering two goodness-of-fit tests: Cramer-von Mises and Chi-square tests. Finally, system reliability of the gas turbine engine under the failure transition, failure intensity, mission reliability and mean time between failures (MTBF) is also discussed in this study.
Highlights
A range of parameters that should be monitored under such health management applications (HMAs) in aero-space and industrial gas turbine engines are presented with the respective system symptoms and fault correlations in [3]
A large number of sensors are required to estimate system states and parameters. Even though these challenges can partially be addressed by estimating a subset of health parameters [22] and incorporating sensor noise into estimation algorithms [23,24] with additional disturbance attention methods [25], the complexities in gas path analysis (GPA) and performance seeking control (PSC) approaches can still degrade the HMAs of gas turbine engines
This study considers on the first category, where the average engine performance as a function of engine cycles is analyzed under the event data (i.e. condition monitoring (CM) data) of a selected gas turbine engine
Summary
Oil & gas production recovery rates have been increased in the recent years due to various technological advancements in the offshore industry. The time period for the ith system failure (from t = 0) The value of the Laplace trend test The significance level The actual and estimate system parameters of the NHPP ( β > 0) The approximate (1 − α) ⋅ 100 percent lower and upper confidence bounds for β The unbiased estimator for β The significance level the expected number of failures in the i-th data interval of the Chi-square test The actual and estimated system parameters (i.e. Failure rate) of the NHPP ( λ > 0) The approximate (1 − α) ⋅ 100 percent lower and upper confidence bounds for λ The system failure intensity The designated confidence coefficients for the two sided (1 − α)⋅ 100 percent confidence intervals of the MTBF The value of the Chi-square test The value of a Chi-square distribution in maximum likelihood estimation (MLE) A statistical distribution approximately a normal distribution with mean 0 and variance 1 and gas industry is struggling to identify critical operational reliability requirements for aging offshore facilities under appropriate cost-effective maintenance actions. I.e. diagnostic and prognostic challenges and evaluate to maintenance policies in offshore power plants, can be two important applications, where the outcome of this study can be used
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