Hernández, ÁngelaCastejón Sisamón, CristinaGarcía Prada, Juan CarlosPadrón, IsidroNicolas Marichal, Graciliano2021-09-082021-09-082019-08-12Advances in mechanical engineering, 11(8), Aug. 2019, 10 p.1687-81401687-8140 (online)http://hdl.handle.net/10016/33248A great investment is made in maintenance of machinery in any industry. A big percentage of this is spent both in workers and in materials in order to prevent potential issues with said devices. In order to avoid unnecessary expenses, this article presents an intelligent method to detect incipient faults. Particularly, this study focuses on bearings due to the fact that they are the mechanical elements that are most likely to break down. In this article, the proposed method is tested with data collected from a quasi-real industrial machine, which allows for the measurement of the behaviour of faulty bearings with incipient defects. In a second phase, the vibrations obtained from healthy and defective pieces are processed with a multiresolution analysis with the purpose of extracting the most interesting characteristics. Particularly, a Wavelet Packets Transform processing is carried out. Finally, these parameters are used as Genetic Neuro-Fuzzy inputs; this way, once it has been trained, it will indicate whether the analyzed mechanical element is faulty or not.10engThe Author(s) 2019Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 LicenseAtribución 3.0 EspañaBearingsFault diagnosisGenetic neuro-fuzzyMultiresolution analysisVibrationWaveletresearch articleIngeniería Mecánicahttps://doi.org/10.1177%2F1687814019831185open access1810Advances in Mechanical Engineering11AR/0000024762