Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method

Ahmed Kibria,Krystel K Castillo-Villar,Harry Millwater

doi:10.1155/2015/813565

Ahmed Kibria, Krystel K Castillo-Villar + Show 1 more

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https://doi.org/10.1155/2015/813565

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Abstract

The reliability modeling of a module in a turbine engine requires knowledge of its failure rate, which can be estimated by identifying statistical distributions describing the percentage of failure per component within the turbine module. The correct definition of the failure statistical behavior per component is highly dependent on the engineer skills and may present significant discrepancies with respect to the historical data. There is no formal methodology to approach this problem and a large number of labor hours are spent trying to reduce the discrepancy by manually adjusting the distribution’s parameters. This paper addresses this problem and provides a simulation-based optimization method for the minimization of the discrepancy between the simulated and the historical percentage of failures for turbine engine components. The proposed methodology optimizes the parameter values of the component’s failure statistical distributions within the component’s likelihood confidence bounds. A complete testing of the proposed method is performed on a turbine engine case study. The method can be considered as a decision-making tool for maintenance, repair, and overhaul companies and will potentially reduce the cost of labor associated to finding the appropriate value of the distribution parameters for each component/failure mode in the model and increase the accuracy in the prediction of the mean time to failures (MTTF).

Highlights

There are several failure modes in gas turbine engines
The method can be considered as a decision-making tool for maintenance, repair, and overhaul companies and will potentially reduce the cost of labor associated to finding the appropriate value of the distribution parameters for each component/failure mode in the model and increase the accuracy in the prediction of the mean time to failures (MTTF)
This paper presented a simulated annealing-based optimization method to minimize the discrepancy of historical and simulated percentages of failures in a turbine engine model

Summary

Introduction

There are several failure modes in gas turbine engines. The failure rates of different components are recorded throughout the life of the engine. Upon obtaining the failure rates of such components, the first step is to conduct a statistical distribution fitting to the initial data per component or failure mode. The mean percentage of failures computed while considering the fitted statistical distribution may not be similar to the historical data due to the lack of quantity or quality of the data. A common practice is to perform some adjustments in the distribution parameters based on human intelligence and the experience of the engineers. There is no formal methodology to approach this complex problem

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