Bearing faults classification based on wavelet transform and artificial neural network
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ORIGINAL ARTICLE
Bearing faults classification based on wavelet transform and artificial neural network Widad Laala1
•
Asma Guedidi2 • Abderrazak Guettaf2
Received: 13 November 2018 / Revised: 15 July 2020 / Accepted: 7 October 2020 Ó The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2020
Abstract The most common types of induction rotating machine failures are the mechanical faults induced by misalignment, mechanical imbalance and bearing fault. It is well known that the vibration is the best and the earliest indicator of arising mechanical defect. Thus, this paper presents a novel practical bearing fault diagnosis method based on wavelet package decomposition (WPD) associated with neural network. Firstly, the raw signal is segmented by the use of WPD to a set of sub-signals (coefficients futures). Then, the energy related to the most sensible coefficients that contained the greatest dominant fault information is selected as a distinctive feature fault. The analysis results show that this fault indicator varies under different loads and states (healthy or defective). In order to automate the detection and the location of bearing defect, this feature can be used as an input to the artificial neural network. The proposed approach is capable of discriminating faults from four conditions of rolling bearing, the healthy bearing and the three different types of defected bearings: outer race, inner race, and ball. The experimental results prove the effectiveness of this approach. & Widad Laala [email protected] Asma Guedidi [email protected] Abderrazak Guettaf [email protected] 1
De´partement de Ge´nie E´lectrique, Laboratoire de Ge´nie E´lectrique de Biskra (LGEB), Universite´ de Biskra, BP 145 RP, 07000 Biskra, Alge´rie
2
De´partement de Ge´nie E´lectrique, Laboratoire de Mode´lisation Des Syste`mes E´nerge´tiques (LMSE), Universite´ de Biskra, BP 145 RP, 07000 Biskra, Alge´rie
Keywords Fault diagnosis Rolling element bearing Wavelet package decomposition (WPD) Artificial neural network (ANN)
1 Introduction It’s well known that the induction machine (IM) dominates the field of electromechanical energy conversion. These machines find a wide role in most industries in particular in the electric utility industry as auxiliary drives in central power plants of power systems, as well as restricted role in low MVA power supply systems as induction generators, mining industries, petrochemical industries, as well as in aerospace and military equipment (Benbouzid and Kliman 2003). However, a continuous monitoring system is mandatory, due to the possibility of unexpected defect on them that may lead to an interruption of production and a heavy economic loss. One of the main critical parts in the IM is the rolling bearing elements (Yu et al. 2018). Failure survey has reported that the percentage failure by bearing faults represent around 41% of total IM failu
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