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https://open.uns.ac.rs/handle/123456789/7368
Title: | Gearbox faults identification using vibration signal analysis and artificial intelligence methods | Authors: | Zuber, Ninoslav Bajrić R. Šostakov R. |
Issue Date: | 8-Jan-2014 | Journal: | Eksploatacja i Niezawodnosc | Abstract: | The paper addresses the implementation of feature based artificial neural networks and vibration analysis for the purpose of automated gearbox faults identification. Experimental work has been conducted on a specially designed test rig and the obtained results are validated on a belt conveyor gearbox from a mine strip bucket wheel excavator SRs 1300. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for self-organized feature maps and based on the results a reduced set of vibration features are used as inputs for supervised artificial neural networks. Two typical gear failures were tested: worn gears and missing teeth. The achieved results show that proposed set of vibration features enables reliable identification of developing faults in power transmission systems with toothed gears. | URI: | https://open.uns.ac.rs/handle/123456789/7368 | ISSN: | 15072711 |
Appears in Collections: | FTN Publikacije/Publications |
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