1357 KB40 KB199920252015Jiang, RuiLi, ZhuangLiu, YibingMa, ZhiyongTeng, Weištevilka:1letnik:61str. 63-73, SI 10ISSN:0039-2480COBISSID:13856539URN:URN:NBN:SI:doc-XH25YQ1VenZveza strojnih inženirjev in tehnikov Slovenije et al.Strojniški vestniknevronska mrežaprenosniki močiprepoznavanje vzorcevrelativna energija valčkovteorija adaptivne resonancezaznavanje napakCrack fault detection for a gearbox using discrete wavelet transform and an adaptive resonance theory neural network|In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands. The changes of sub-band energy are thought to be caused by the crack fault. Therefore, the relative wavelet energy is proposed as a feature. An artificial neural network is introduced for the detection of crack faults. Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults. An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector. The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault. The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth faultZgodnje zaznavanje napak na prenosnikih moči je pogoj za preprečitev usodnih okvar strojev, izgub v proizvodnji in telesnih poškodb. Proces prehoda prenosnika iz normalnega stanja v stanje napake poteka počasi. Zaradi omejitev, ki jih postavljata mehanska zgradba in delovno okolje, je težko meriti spremembe stanj pri oblikovanju razpoke zgolj na osnovi vizualne ocene značilnosti, izluščenih iz signala vibracij. Zato potekajo raziskave nove metode zaznavanja razpok, ki omogoča zgodnjo diagnostiko napak na prenosnikih. Signali vibracij iz prenosnikov so nestacionarni in nelinearni, diskretna valčna transformacija (DWT) pa je učinkovito orodje za obdelavo takšnih nestacionarnih in nelinearnih signalov. V praksi pri zaznavanju napak na prenosnikih manjkajo primerki znanih napak, ki bi jih bilo mogoče uporabiti za učenje nadzorovane nevronske mreže. Nevronska mreža z adaptivno resonančno teorijo (ART) je metoda prepoznavanja vzorcev brez znanih primerkov za učenje. Članek združuje transformacijo DWT in nevronsko mrežo ART za zaznavanje razpok na prenosnikuTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo