The aerocraft health diagnosis based on fuzzy C-means clustering analysis and acoustic emission technique

Abstract

To diagnose effectively the aerocraft key structure components fatigue damages, discover in good time hidden trouble, avoid fearful accident occurring, the advanced acoustic emission (AE) technique is used to monitor the aerocraft stabilizer health state and get the AE information. Chaos theory is used to extract the correlation dimension of the AE information and construct the character vector. And the fuzzy C-means clustering health diagnosis arithmetic is designed to diagnose the health state of the aerocraft stabilizer. A new kind of fatigue damage health diagnosis approach to the aerocraft stabilizer, based on AE information correlation dimension and fuzzy C-means clustering analysis arithmetic, is proposed in this paper. Experiments show that the approach has good performance to diagnose the fatigue crack of the aerocraft stabilizer. It presents a new approach to diagnose effectively health state of aircraft structure components.