Aircraft Health Diagnosis Method Based on ARMA Model and Probabilistic Neural Network
| Periodical | Advanced Materials Research (Volumes 225 - 226) |
|---|---|
| Main Theme | Advanced Research on Automation, Communication, Architectonics and Materials |
| Edited by | Helen Zhang, Gang Shen and David Jin |
| Pages | 527-530 |
| DOI | 10.4028/www.scientific.net/AMR.225-226.527 |
| Citation | Jian Guo Cui et al., 2011, Advanced Materials Research, 225-226, 527 |
| Online since | April, 2011 |
| Authors | Jian Guo Cui, Bo Han Song, Shi Liang Dong, Hai Gang Liu, Qing Zhao |
| Keywords | AIC, ARMA Model, Health Diagnosis, Parameter Estimation, PNN |
| Price | US$ 28,- |
In order to diagnose the health state of Aircraft effectively, a new method based on ARMA Model and probabilistic neural network(PNN) is proposed in this paper. First, an ARMA model is built using the original acoustic emission signal of aircraft crucial components, then use the autoregressive approximation theory to estimate model parameters, and order of the model is calculated according to Akaike Information Criterion(AIC). Use the autoregressive parameters to build feature vectors, then the probabilistic neural network is used to carry out the recognition of these feature vectors, and the health state of aircraft crucial components is effectively diagnosed. After the application on certain type of real aircraft, this method is proved to be capable of detecting the fatigue crack on crucial structural components. And we can conclude that the method is an effective way to carry out aircraft health diagnosis.
