Depot Institutionnel de l'UMBB >
Publications Scientifiques >
Publications Internationales >
Veuillez utiliser cette adresse pour citer ce document :
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7102
|
Titre: | Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier |
Auteur(s): | Ikhlef, Boualem Rahmoune, Chemseddine Bettahar, Toufik Benazzouz, Djamel |
Mots-clés: | Rotary machines Gearboxes Fault detection Feature extraction Selection Optimization Classification |
Date de publication: | 2021 |
Editeur: | SAGE Publications |
Collection/Numéro: | Advances in Mechanical Engineering/ Vol.13, N°8 (2021);pp. 1-12 |
Résumé: | Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence,
their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault
detection and classification. However, most of the works are carried out under constant speed conditions, while gears
usually operate under varying speed and torque conditions, making the task more challenging. In this paper, we propose
a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures
under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet
Packet Transform (MODWPT) to extract the AM-FM modes. Next, time domain features are calculated from each
mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant
and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest
(RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this
method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of
the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating
condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had
been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to
handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency |
URI/URL: | https://doi.org/10.1177/16878140211043004 https://journals.sagepub.com/doi/full/10.1177/16878140211043004 http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7102 |
ISSN: | 1687-8132 1687-8140 Electronic |
Collection(s) : | Publications Internationales
|
Fichier(s) constituant ce document :
|
Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.
|