Adaptive On-Wing Gas Turbine Engine Performance Estimation

A key technological concept for producing reliable engine diagnostics and prognostics exploits the benefits of fusing sensor data, information, and/or processing algorithms. In this paper, we consider a real-time physics based model of a commercial turbofan engine called STORM: self tuning on-board, real-time engine model. The STORM system provides a means for tracking engine module performance changes in real-time. However, modeling error can have a corruptive effect on STORM´s estimation of performance changes. Fusing an empirical neural network based model with STORM forms a unique hybrid model of the engine called enhanced STORM (eSTORM). This approach can eliminate the STORM engine diagnostic errors. A practical consideration for implementing the hybrid engine model, involves the application of some form of sequential model building to construct and specify the empirical elements. A methodology for constructing the empirical model (EM) in a sequential manner without the requirement for storing all of the original data has been developed. This paper describes the development of the adaptive hybrid model scheme for a commercial turbofan engine. This adaptive hybrid-modeling scheme has been implemented in real-time on an intelligent automation corporation (IAC) computational platform. Model performance achieved with the automated update algorithm using real on-wing commercial aircraft engine data will be presented.