694 KB33 KB199920252016Do, Van TuanNguyen, Le Cuongletnik:62številka:nr. 5str. 281-290ISSN:0039-2480COBISSID_HOST:14643227URN:URN:NBN:SI:doc-22L36XOIenZveza strojnih inženirjev in tehnikov Slovenije etc.Strojniški vestnikanaliza ovojniceempirična dekompozicija oblikfunkcije lastnih oblikHilbert-Huangove transformacijeimenska frekvencaodkrivanje napak na ležajihAdaptive empirical mode decomposition for bearing fault detection|Many techniques for bearing fault detection have been proposed. Two of the most effective approaches are using envelope analysis and the empirical mode decomposition method (EMD), also known as Hilbert-Huang transform (HHT), for vibration signals. Both approaches can detect the bearing fault when the vibration data are not strongly disturbed by noise. In the approach using EMD method, the EMD algorithm is used to decompose the vibration data into components with a well-defined instantaneous frequency called intrinsic mode functions (IMFs). Then a spectral analysis is used for selected IMFs to indicate the appearance of nominal bearing defect frequencies (nominal frequencies), which are caused by bearing faults. However, when the data are strongly disturbed by noise and other sources, the approach can be failed. The EMD algorithm generates IMFs itself; hence, the IMFs will also contain both a fault signal part and other components. It becomes more severe when the other components are dominant and have significant amplitudes near the same frequencies as the fault signal part. Moreover, in the IMF extracting process, the EMD methods keeps removing the low-frequency components until the residual is an IMF; therefore, until the IMF is found, some of the fault signal parts can be removed and will appear in the next IMFs. Therefore, it must be emphasized that the energy of the fault signal part can spread in some IMFs that will lead the detecting faulty features in any of those IMFs to be weak. In this paper, we address the weakness of the EMD method for bearing fault detection by introducing an adaptive EMD (AEMD). The AEMD algorithm is intended to generate IMFs so that one of them contains most of the energy of the fault signal part; thus, it assists our model to detect the bearing fault better. Moreover, the bearing fault detection model using the AEMD method with simulation data is compared with those of using envelope analysis and the latest version of the EMD, called an ensemble EMD algorithm. An application study of bearing fault detection with AEMD method is also carried outČlanek predstavlja poskus izboljšanja učinkovitosti odkrivanja napak na nizkohitrostnih ležajih z adaptivno empirično dekompozicijo oblik (AEDO) v primerjavi z izvirno metodo EDO in analizo ovojnic. Izvirna metoda EDO se včasih ne izkaže najbolje, če so podatki močno obremenjeni s šumom ali z drugimi viri. Težave se še potencirajo, če v signalu dominirajo druge komponente in imajo signifikantne amplitude v bližini frekvenc dela signala, ki izvira iz napake. Metoda EDO v procesu ekstrahiranja funkcij lastnih oblik (IMF) odstranjuje nizkofrekvenčne komponente, dokler ne pride do ostanka, ki postane IMF. Dokler IMF ni najdena, se lahko torej odstrani tudi določen del signala napake in se nato pojavi v naslednjih IMF. Zato se lahko zgodi, da se energija signala napake razširi po več IMF in v tem primeru bo možnost zaznavanje lastnosti napak v katerikoli od teh IMF majhna. Po predlagani novi metodi se funkcije IMF ustvarijo tako, da ena od njih vsebuje večino energije signala napakeTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo