Lima, Fernando P.A. [UNESP]Chavarette, Fábio R. [UNESP]Souza, Simone S.F. [UNESP]Lopes, Mara L.M. [UNESP]2018-12-112018-12-112017-04-25Probabilistic Prognostics and Health Management of Energy Systems, p. 203-219.http://hdl.handle.net/11449/170367This chapter presents a Wavelet-Artificial Immune System (WAIS) algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, the wavelet module is used to transform the signals into the wavelet domain. Afterward, a negative selection artificial immune system performs the diagnosis via identifying and classifying the failures. The main application of this methodology is in the auxiliary structures inspection process in order to identify and characterize the flaws as well as assist in the decision making process that is aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam that represent an aircraft structure such as a wing. The proposed algorithm presented good results, with 100% matching in detecting and classifying of the failures tested. The results demonstrate the robustness and accuracy of the methodology.203-219engAeronautical structuresArtificial intelligenceMonitoring and fault identificationWavelet-artificial immune systems (WAIS)Capítulo de livro10.1007/978-3-319-55852-3_13Acesso restrito2-s2.0-8503373037357233598853653390000-0002-1203-7586