A New MLP Approach for the Detection of the Incipient Bearing Damage

Autor:	SEKER, S., KARATOPRAK, E., SENGULER, T.
Jazyk:	angličtina
Rok vydání:	2010
Předmět:	bearing damage vibration analysis MLP neural networks feature extraction condition monitoring Electrical engineering. Electronics. Nuclear engineering TK1-9971 Computer engineering. Computer hardware TK7885-7895
Zdroj:	Advances in Electrical and Computer Engineering, Vol 10, Iss 3, Pp 34-39 (2010)
Druh dokumentu:	article
ISSN:	1582-7445 1844-7600
DOI:	10.4316/AECE.2010.03006
Popis:	In this study, it is aimed to track the aging trend of the incipient bearing damage in an induction motor which is subjected to an accelerated aging process. For this purpose, a new Multilayer perceptron (MLP) neural network approach is introduced. The input signals are extracted from power spectral densities (PSD) of the vibration signals taken from a 5-HP induction motor. Principal component analysis (PCA) has been applied to select the best possible feature vectors as a dimensionality reduction purpose. Variance and entropy values are used as the targets of the MLP network. The healthy motor condition was modelled by the MLP network considering all load conditions. The results showed that the incipient bearing damage was clearly extracted by the oscillations of the MLP output error.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/15cf6ae2223d478a8f7ab89432c5ee41 Zobrazit plný text záznamu View record in DOAJ