@article {Ümütlü:2016:0736-2935:2718,
title = "Pitting Detection in a Worm Gearbox Using Artificial Neural Networks",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2016",
volume = "253",
number = "6",
publication date ="2016-08-21T00:00:00",
pages = "2718-2726",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2016/00000253/00000006/art00100",
author = "{\"U}m{\"u}tl{\"u}, Rafet Can and Ozturk, Hasan and Kiral, Zeki",
abstract = "Diagnosis of worm gears faults using vibration analysis is difficult, for this reason; there have been quite little publications, although worm gears are used significant machines in assorted industrial fields. Whenever a defect occurs in a worm system (e.g. pitting, abrasive wear)
the performances of the gears deteriorate. Therefore, transmission of motion and power cannot be transferred as demanded. As a result, occurrence of fatal defects becomes inevitable. This paper focuses upon the early detection of localized pitting damages in a worm gearbox using artificial
neural networks (ANN) and vibration analysis. Worm gear vibrations are acquired from an experimental rig utilizing a 1/15 worm gearbox. Statistical parameters of vibration signals in the time and frequency domains are used as an input to classifier ANN for multi-class recognition.",
}