@Article{OliveiraMilaKoit:2013:FaDeIs,
               author = "Oliveira, {\'E}lcio Jeronimo de and Milagre da Fonseca, Ijar and 
                         Koiti Kuga, Helio",
          affiliation = "{Instituto de Aeron{\'a}utica e Espa{\c{c}}o} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Fault Detection and Isolation in Inertial Measurement Units Based 
                         on -CUSUM and Wavelet Packet",
              journal = "Mathematical Problems in Engineering",
                 year = "2013",
               volume = "2013",
                pages = "1--10",
                 note = "Setores de Atividade: Transporte a{\'e}reo, Transporte 
                         terrestre.",
             abstract = "The aim of this paper is to present a fault detection algorithm 
                         (FDI) based on signal processing techniques developed for an 
                         inertial measurement unit (IMU) with minimal redundancy of fiber 
                         optic gyros. In this work the recursive median filter is applied 
                         in order to remove impulses (outliers) arising from data 
                         acquisition process and parity vector operations, improving the 
                         fault detection and isolation performance. The FDI algorithm is 
                         divided into two blocks: fault detection (FD) and fault isolation 
                         (FI). The FD part of the algorithm is used to guarantee the 
                         reliability of the isolation part and is based on parity vector 
                         analysis using [X.sup.2]-CUSUM algorithm. The FI part is performed 
                         using parity space projection of the energy subbands obtained from 
                         wavelet packet decomposition. This projection is an extension of 
                         clustering analysis based on singular value decomposition (SVD) 
                         and principal component analysis (PCA). The results of the FD and 
                         FI algorithms have shown the effectiveness of the proposed method, 
                         in which the FD algorithm is capable of indicating the low-level 
                         step bias fault with short delay and a high index of correct 
                         decisions of the FI algorithm also with low-level step bias 
                         fault.",
                  doi = "10.1155/2013/869293",
                  url = "http://dx.doi.org/10.1155/2013/869293",
                 issn = "1024-123X",
                label = "lattes: 0897606176121914 2 OliveiraMilaKoit:2013:FaDeIs",
             language = "en",
        urlaccessdate = "20 maio 2024"
}