Abstract: In order to improve the accuracy of aeroengine component fault diagnosis and the generalization ability which is either based on a model or data driven, a method of engine component fault diagnosis based on decision fusion of self tuning weight is proposed. The sensor output is fed to two different prognostic modules to acquire component performance parameters simultaneously. Estimated performance parameters are obtained by Kalman filtering and adaptive genetic algorthms support vector regression (AGA-SVR) respectively. Then the component performance parameters are fused by quantum particle swarm optimization (QPSO) of self tuning weight for fault decision. Simulation on a turbofan engine shows that compared with either of the model-based or data-driven module, this method improves significantly the accuracy of fault diagnosis.

Key words: aeroengine, fault diagnosis, self tuning model, data driven, Kalman filtering, adaptive genetic algorithms, support vector regression, quantum particle swarm optimization