Research on health state monitoring and diagnosis approach for aerocraft structure

To monitor effectively the aerocraft structure fatigue damages, discover in good time hidden trouble, avoid fearful accident occurring, the advanced acoustic emission (AE) technology is used to monitor the aerocraft structure health state. The variance of the wavelet packet decomposition coefficients to the gathered AE signals are extracted as the aerocraft structure robust health state feature vectors. And the fuzzy Kohonen cluster network health state monitoring system is designed to diagnose the aerocraft health state. A new kind of aerocraft structure health state diagnosis approach, based on AE information variance feature vector and fuzzy cluster network, is proposed in this paper. Experiments show that the approach has good performance to monitor and diagnose the fatigue crack in the aerocraft structure components. It presents a new approach to monitor and diagnose the aircraft structure health state.