Depot Institutionnel de l'UMBB >
Publications Scientifiques >
Publications Internationales >
Veuillez utiliser cette adresse pour citer ce document :
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13604
|
Titre: | Enhancing air compressors multi fault classification using new criteria for Harris Hawks optimization algorithm in tandem with MODWPT and LSSVM classifier |
Auteur(s): | Rahmoune, Chemseddine Amine Sahraoui, Mohammed Gougam, Fawzi Zair, Mohamed Meddour, Ikhlas |
Mots-clés: | Fault diagnosi Air compressor Multi-fault classificatio Feature selectio Harris Hawks optimizatio MODWPT LSSVM classifier Industrial systems Industry 4.0 Machine learning Stability Signal processing accuracy fault detection |
Date de publication: | 2023 |
Editeur: | SAGE |
Collection/Numéro: | Advances in Mechanical Engineering/ Vol. 15, N° 12(Dec. 2023);PP. 1-14 |
Résumé: | The evolution of industrial systems toward Industry 4.0 presents the challenge of developing robust and accurate models. In this context, feature selection plays a pivotal role in refining machine learning models. This paper addresses the imperative of accurate fault diagnosis in industrial systems, focusing on air compressors. These systems, vital for efficient operations, demand early fault detection to prevent performance degradation. Conventional methods often encounter challenges due to the occurrence of similar failure patterns under comparable conditions. To address this limitation, our approach delves into a more complex scenario, where air compressors operate under diverse fault conditions. This study introduces novel feature selection criteria achieved through a fusion of the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT), the Harris Hawks Optimization (HHO) algorithm, and the Least Squares Support Vector Machine (LSSVM) classifier. The synthesis of these components aims to bolster the multi-fault diagnosis accuracy and stability for each fault class. The evaluation focuses on key statistical metrics—minimum, maximum, mean, and standard deviation. Experimental outcomes underscore the method’s superiority over traditional feature selection techniques. The approach excels in accuracy and stability, particularly across various fault categories, affirming the efficacy and resilience of the new criteria. The symbiotic integration of MODWPT, HHO, and LSSVM within our framework highlights its potential to elevate classification performance in the realm of industrial fault diagnosis. |
URI/URL: | https://doi.org/10.1177/16878132231216208 https://journals.sagepub.com/doi/10.1177/16878132231216208 http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13604 |
ISSN: | 1687-8132 |
Collection(s) : | Publications Internationales
|
Fichier(s) constituant ce document :
|
Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.
|