Evolutionary Algorithm for Enhanced Gas Path Analysis in Turbofan Engines

Abstract

Adaptive modelling (AM) based Gas Path Analysis (GPA) is a powerful diagnostic and prognostic technique for turbofan engine maintenance. This involves the assessment of turbofan component condition using thermodynamic models that can iteratively adapt to measurements values in the gas path by changing component condition parameters. The problem with this approach is that newer turbofan engines such as the General Electric GEnx-1B have fewer gas path sensors installed causing the AM equation systems to become underdetermined. To overcome this problem, a novel approach has been developed that combines the AM model with an Evolutionary Algorithm (EA) optimization scheme and applies it to multiple operating points. Additionally, these newer turbofan engines provide performance data continuously during flight. Information on variable geometry and bleed valve position, active clearance control state and power off-take is included and can be accounted for to further enhance AM model accuracy. A procedure is proposed where the selection of operating points is based on steady-state stability requirements, cycle model operating point uncertainty and parameter outlier filtering. The Gas turbine Simulation Program (GSP) is used as the non-linear GPA modelling environment. A Multiple Operating Point Analysis (MOPA) is chosen to overcome the problem of underdetermination by utilizing multiple data sets at different operating points. The EA finds the best fit of health parameter deviations by minimizing the multi-point objective function using the GSP AM model. A sub-form of the EA class named Differential Evolution (DE) has been chosen as the optimizer. Like all EAs, DE is a parallel direct search method in which a population of parameter vectors evolves following genetic operations towards an optimum output candidate. The resulting hybrid GPA tool has been verified by solving for different simulated deterioration cases of a GSP model. The tool can identify the direction and magnitude of condition deviation of 10 health parameters using 6 gas path sensors. It has subsequently been validated using historical in-flight data of the GEnx-1B engine. It has demonstrated successful tracking of engine component condition for all 10 health parameters and identification of events such as turbine blade failure and water washes. The authors conclude that the tool has proven significant potential to enhance turbofan engine condition monitoring accuracy for minimizing maintenance costs and increasing safety and reliability.